#algorithms have been teaching people that making people angry is the way to get any traction or clout

[tinystrawberryshifter]

Bro what happened to "Don't tag your hate" ??? Rage bait culture has got to die actually.

[kamadevva]

analysis/interpretation on the ending scene of ōoku

I wanted to get my thoughts out about FGO event Ōoku now that the event is over, I’ve got no idea how Tumblr algorithm works but hopefully someone sees this and adds their own thoughts since I’d love to have discussion on this since I’ve got MANY words about this event tbh.

The main scene I wanted to go over was the ending at least, with the small discussion Kama and Kiara have with one another— to preface this, I sort of wanted to acknowledge for a fact Ōoku is NOT without flaw and is a pretty poorly written event in certain aspects, and I 100% understand why people are frustrated about how cruel this event treats Kama!! To be honest, I think the reason why I interpret the ending scene the way I do is a desperate attempt to give myself some closure over giving Kama some healing, but I wanted to see if anyone agreed first.

Anyways, about the scene itself: when reading through the ending, I personally thought Kiara’s send-off, although obviously still some form of “punishment” and “karmic retribution” was actually motivated by a small, good-intended desire to give Kama a chance of healing at Chaldea.

I sort of wanted to point out this scene in particular, which struck me differently compared to the reactions of other characters:

As observed by other people, I think it’s pretty sucky that most other characters basically congratulated Parvati for forcing a second traumatic experience onto Kama and barely turned an eye to how cruel the usage of her Noble Phantasm actually was (I’m aware Kama needed to be taken down sooner or later since, had their plan succeeded after all, they would’ve never gotten the chance to properly heal, but I still think it’s unnecessarily cruel to throw a second painful incineration specifically led by one of the main sources of their trauma in the first place).

But there is one character who DOES point out their concern for Kama’s fate, and that’s Sessyoin Kiara herself.

Kiara, with no doubt, is a wicked woman with flawed morals— she herself makes it a point to state this and does confirm this is her own way of giving Kama “punishment”.

And yet, what strikes me as important is that Kiara is the only character to point out that Kama had brought their fate on themself— she is, as far as I’m concerned, the only character to observe their motives and morals in its truest form and vaguely attempt to connect and empathize with such a mindset. Kiara also seems to be the only one to pick up on how Kama has a burning hatred for themself in this scene, while other characters of Ōoku tend to pass Kama off as arrogant, egoistical, and haughty.

In a way, I personally believe Kiara noticing the depth behind Kama’s personality and motives is already an act that no other characters of Ōoku provide for (especially Parvati, like wtf?). It reads as a vague attempt of both sympathy and empathy to give Kama a chance to be heard, and even if it is coupled with Kiara’s narcissistic and rather sadistic behavior, I do believe Kiara made nonetheless a small attempt to give Kama a sense of understanding. There even seems to be a vague hint of concern (Kiara later states “Even I would not go that far” regarding the incineration) towards Kama’s self-destructive behavior. Given Kiara’s masochistic tendencies, I only think that line is further important that she full-on admits Kama’s self-hatred actually surpasses her own limits for self-preservation.

Kiara herself actually points out Kama, in the end, has a rather pure and benevolent soul, stating:

While this could be read as Kiara attempting to mock Kama, especially given their angry reaction after— I like to believe that deep down inside, Kiara genuinely did acknowledge the tragedy of Kama’s situation. I once again want to reiterate the fact that scarcely any other character in Ōoku offers this sympathy, and Kiara is, once more, the only person to attempt to describe and observe Kama as an empathetic victim who was mistreated cruelly all throughout.

Then, once Kiara begins the process of assimilating Kama into the servant summoning system:

Punishment or not, Chaldea is exactly an environment Kama needs to heal, and I believe Kiara herself is aware of that— of course, I don’t believe Kiara was doing this out of the goodness of her heart, but there seems to be a hint of genuine concern for Kama behind the statement of redemption and “remaining misunderstood forever”.

Without Kiara sending Kama to Chaldea, it’s possible the tired god of love may have never been able to find peace and healing— more than likely, it feels as if Kama had fully intended to drift in their universe without end, seeing themselves as nothing more than a “loser” with a pathetic personality.

Described by their bond lines, and later their interlude, the type of people Kama needs to heal is found in Ritsuka— they need someone to (metaphorically) “teach” them love, for even if they’re aware of everything about it, they have long forgotten the positive, simple, and wholesome experiences associated with love. They’re aware of its existence, just as they’re aware of the love they held for Rati and Vasanta— but no longer feel any emotional connection or feeling to love in its purest form, and that is where characters like Ritsuka come into play. Chaldea is a place where Kama can “relearn” these experiences once more, and begin their path of healing— and I genuinely believe Kiara was fully aware of this while sending Kama to Chaldea.

Would also like to point out that one of the main motifs of this event was the whole lesson behind Kasuga’s definition of love— rather than endless depravity that spoils people rotten and, as a result, condones evil and sin, love is instead found through a sense of nurturing and guidance— an act of supporting and helping a person grow without necessarily keeping shackles on their development.

Kiara’s actions here seems to be vaguely reminiscent of the love Kasuga feels for the Ōoku and the shoguns— even if she does not presently state this, she similarly desires to reach an understanding with Kama and send them on this path of healing and development. For a woman that ultimately can only love herself, I think it is still important for both of their characters that Kiara was nonetheless capable of hinting, at the very least, empathy for Kama and a desire to give them healing closure no other character in Ōoku attempts to provide.

Again, I don’t deny that Kiara did not have the purest intentions by toying with Kama at the end, and I still believe Ōoku is poorly written in this regard of unnecessarily torturing a trauma victim— but, just as Kama was built by tragedy, Kiara underwent similar— even if Kiara herself is presently aware there is no longer “good” in her and she is no longer the holy woman she once was, Kiara still nonetheless desires for good to exist— just not specifically from herself, and linking Kama with Ritsuka was an example of her trying to keep the existence of good morals. This, to me, also felt like a conclusion to Kiara’s whole practice of self-restraint throughout the event— she wants to “guide” Ritsuka into development and eventually take her down, secretly motivating for them to abstain from her, even if she still nonetheless desires to be the one to corrupt them in the end.

I’ve got a big handful of other thoughts on this event (especially as a huge Kama fan), but that’s all I’ll touch on here, since I really wanted to share at least my interpretation of the ending scene and what it meant to both Kama and Kiara. I also really want to discuss this with others, so please feel free to add on! If you don’t agree with any of this, that’s fine too, but I’d still love to hear people’s thoughts nonetheless.

[mafia-bots]

Chatbots have been around for a few decades, originally as a way to bridge the gap between people and machines. These days, chatbots are everywhere, from Facebook Messenger to Slack. The bot ecosystem has exploded in recent years because they're relatively cheap and easy to create.

If you're a digital marketer or a business owner, you can use chatbots to reduce costs, grow organic traffic and generate leads. In this article, I am going to answer the question: how do chatbots work?

 Types of Chatbots

There are two types of chatbots: chatbot platforms and conversational bots. Chatbot platforms are tools that allow you to build a bot without any technical knowledge. They are designed to be easy to use by developers who are not technical. Conversational bots are bots with which you can converse in real time. You can program these bots following industry standards to make them more intelligent.

 How do Chatbots Work?

To understand how chatbots work, let's say that I want to interact with a chatbot named Sarah on Facebook Messenger. I click on the "Send Message" button and write, "Hi Sarah."

Step 1: First, the bot gets my message.

Step 2: Then it sends me a welcome message.

Step 3: I can now chat with Sarah. She will understand what I'm asking by analyzing my messages and replies with intelligent responses. All she needs to know is how to parse language like an expert human would (AI).

Chatbots understand human language with machine learning and natural language processing algorithms. To train a chatbot, you give it examples of various conversations it might have. The more conversations she has, the wiser she'll become.

To make Sarah more intelligent, I could also teach her to respond to commands regarding a restaurant reservation, a purchase on my wishlist or my delivery orders. She would do all of this through a series of example conversations provided by me.

 How do Chatbots Work with Artificial Intelligence Tools?

Chatbots are designed to mimic human behavior to give customers a real-life experience. They are also trained to understand human language, emotions and reactions. For instance, they can tell if you're angry or amused by your attitude.

 Here are some tools that are used to build the chatbot.

Natural Language Processing (NLP) algorithms translate human language into digital text and then into actionable tasks for the chatbot. Machine learning helps the bot to learn from each interaction with a human and improve its performance each time.

 How to Create a Chatbot: Tools and Platforms

Chatbots use artificial intelligence frameworks like Facebook's Wit.ai, Microsoft Bot Framework and IBM Watson to understand human emotions and responses based on keywords, phrases or voice commands.

To create a chatbot, you first need a developer tool to create a bot on the platform of your choice. You can use Facebook Messenger, Slack or similar services. You then need to create the conversation flow with the chatbot, which includes conversations with humans and bots as well as smart replies from machine learning algorithms.

 How do Chatbots Work in Real Life?

Chatbots that are built on platforms with artificial intelligence can converse with humans in natural language. These bots are able to understand the context of the conversation and respond accordingly. Most transactions can be handled with chatbots by using keywords, phrases or voice commands.

You will find chatbots everywhere: from customer service and support to personalized shopping experiences and virtual assistants like Siri or Cortana.

 Chatbot Platforms

There are many existing chatbot platforms that can help you build a bot in just a few clicks. All you need to do is to download the platform and connect it to your application, website or social media account.

 How do Chatbots Work in the Future?

Facebook recently made chatbots more "intelligent" by allowing them to communicate with users on Messenger without any human interaction. This means that brands can be used for advertising on Facebook Messenger without having to worry about the company maintaining an active account.

Chatbots are still in their infancy, so there is a lot of room for improvement. Soon, we will reach the point where chatbots can understand human language, personality and emotions better than humans themselves.

[solarpunkvegan]

Please cite your sources. Listen, I believe you, and if you're wrong, it's most likely because you believe it yourself. Some people do intentionally lie, of course, but I don't believe it's the majority.

I try to give everyone the benefit of the doubt. Most people are ignorant not stupid, meaning they lack knowledge, not the ability to understand that knowledge. We thought the internet age would bring about a second enlightenment because everyone would have access to knowledge. But then disinformation, misinformation, and lies pervaded it.

When you try to teach me sometime new I didn't know, I'm excited, I love learning. I trust that you probably aren't out to lie to me. But if we don't cite our sources, how can we ever stave off our post-truth age where facts are meaningless?

I know you're busy, I know it's not your "job" to do the research for others. But when there's too much info out there it becomes confusing and overwhelming. If you want to educate you need to cite sources, even if you find that people refuse to click on them, which happens to me a lot. At least you've done your part. The knowledge is right there at their fingertips, if they choose not to click it, they know they're choosing to not listen to your side. If you cite sources, even if no one clicks them, it appears to anyone looking like you've done your research, which you have, and it changes the way they look at your content. The person you're talking to might refuse to listen, but you've still planted a seed. Anyone else watching sees that you not only were calm, logical, kind, and posted sources, but if they click the sources themselves they can see that the person arguing against you didn't bother to click the sources themselves.

Yes, people have said to me, literally, "I can just post an opposite source!" All. The. Time.

And yes, when I nicely asked one of them to post their source they blocked me. (BTW, this was about masks and was with another animal rights activist, someone who's been vegan for over a decade and is over 40 years old. The source I posted was from Dr. Greger, a famous plant-based doctor who donates all his proceeds from book sales and speaking tours to charity, has a free app and website with no ads or corporate sponsors that runs on donations and volunteers. He doesn't sell any pills, diet fads, scams, or other products besides some cute t-shirts that also go into the site maintenance.)

In blocking me she probably also removed my comments which is not ideal. That's what makes Tumblr great in some respects, when they block me they can't delete what I've said. Anyone watching will see what has occurred: one person was calm, kind, and posted sources, the other was angry, mean, and then blocked them. YouTube comments are the same way, the person who made the video can erase comments or turn them off, but they usually don't want to because comments drive the alogoritm and get the video more views. So if we have debates about veganism in the comments of vegan videos we do 3 things:

Boost the video, using the algorithm to our advantage.

Have a debate with someone and potentially change their mind.

Let others see us being calm, logical, rational, kind, and posting credible sources.

Plant seeds for whoever sees any of this, including those we debate with.

I need to stop doing this type of activism myself as it's not good for my mental health and it distracts me from work I think I could do more good with, my YouTube videos and art. It can be grating, it can take a toll, but so can any activism. It may seem like "slacktivism" but if you are someone with less options for activism than others it could be ideal. Not everyone can participate in all forms of activism and different kinds are good for different people. If the way you want to do activism is by opening a vegan bakery, or making vegan art, or donating to vegan organizations, signing petitions, sharing things on social media, etc. all are helpful. But in all endeavors it's best to stay calm, kind, respectful, and to post links to sources and quote those sources too. If this is something you struggle with that's ok, this type of activism may not be for you. If talking to people stresses you out, raises your anxiety, or if you have a hard time staying calm, then that's ok, maybe just do something else with your time and energy.

But if you are going to post, try to make those posts kind and informative. That doesn't mean never being blunt, or not using certain words, or not calling it like it is. It simply means not using ad hominem attacks, attacking people's personality, ridiculing their beliefs, culture, or religion, bringing up things that have nothing to do with the debate at hand, cursing, calling people stupid, calling people "brainwashed" or "hypocrites" or "sheep", focusing on their looks, being ableist, fatphobic, racist, sexist, xenophobic, anti-neurodivergent, etc. Not only is none of that necessary to help the animals, I think it actively makes our job harder and hurts the movement overall. Shame and guilt doesn't work. Negative reinforcement works much less effectively than positive reinforcement. Being cruel to others makes people hate us as a group. Of course that's not fair or justified or right, but it's the way it is. Humans always lump people into categories and then make assumptions based on them. That's how our brains work, sadly. We're all biased. We all experience some cognitive dissonance sometimes. We all judge ourselves and others. But we can work to change that and it starts with you every time you're kind instead of cruel.

Instead of being cruel towards humans and asking them to be kind to animals, we should be kind to humans and ask them to extend that kindness to animals.

You may say "that's some kumbaya bullshit, the real world doesn't work like that" but for starters, that sounds a lot like the argument:

"the world isn't fair, get used to it" that we hear about capitalism. We can choose to change the world to make it fair, or hurt each other because we assume it never can be. The world is cruel because we are all cruel, to some extent. (Yes, some are crueler than others and use their power over others to do cruel things. Yes, some people cause much more harm than others.) We can work to change that to make it kind, instead of assuming it always will be unkind.

And secondly, I believe in being kind to everyone because to me, it's the right thing to do. BUT I also believe it is the most effective praxis when trying to change minds. It's not just about what makes me feel good as some silly hippy or whatever you want to say. No, it's also what I truly believe we need. Based on all the evidence I have absorbed over the years, this is the radical paradigm shift I think we need. (I could be wrong, I'm always learning and my mind is open to being changed.) Our world is based on cruelty. Don't you want to oppose that? And don't you want to "be the change you wish to see"? Doesn't it start with you? If you don't believe the fallacy that "one person can't change anything" then aren't you one person who can change things? We can't control other people. We can't force them to have compassion for others. But we can have compassion ourselves. Isn't that what personal responsibility is about? How do you want to make others feel? What do you want to create in the world? What do you want to add to the human story? You get to decide everyday.

[an-emo-on-emo-site]

time for a dumb critique of stranger things written by a dumb emo 13 year old

aight so since im a massive film/tv nerd lets talk about stranger things 

YOUNG BOY STRANGER THINGS

so basically the first season is nearly perfect imo. the cinematography is phenomenal and every. single. shot. serves the mood of the scene really well. the show has excellent writing that makes teens feel like actual people which a lot of other teen shows fail at, and it can be really funny without actually sacrificing the mood of the show as a whole or detracting from the severity of the situation. it can also be genuinely scary (the first watch through) without relying too much on gore to induce a cheaper “shock scare”. It maintains tension expertly throughout the whole show until the resolution and the ending cliffhanger is a perfect end. There really isnt much more i can say, the entire season is just chef’s kiss

DEMOGORGON 2: ELECTRIC BOOGALOO

while season 2 is still decent and succeeds in some of the aforementioned manners (cinematography and humour), there are quite a few g l a r i n g flaws in the show which kinda take it down quite a few pegs for me. Season one was almost a mystery show disguised as horror, and therefore was a lot more engaging because you wanted to solve the mystery just as much as the characters did. While you were still invested in the show and liked the characters, most of the enjoyment (for me at least) came from trying to figure out what happened to will. While Season 2 does have some of this carry over with the Mind Flayer infection, the mystery never really progresses, they just figure something out in the last hour of the show. Season 2 relies a lot more on the characters to carry the show and keep the audience engaged. While this does work a lot of the time, especially with the dynamics between Dustin and Steve (we stan), a lot of the enjoyment from the show was taken away (for me) when the mystery aspect was toned down. Additionally, there wasn't really any particularly scary threat for the majority of the season. In Season 1 we are aware of the existence of the Demogorgon throughout the show and we are consistently shown that it is a severe threat to the protagonists, but in Season 2 we only really have a threat at the very end (last two episodes), and even those are literally just tiny demogorgons. While they obviously can still be harmful, they’re much tamer considering how hyped up big boy demogorgon was in the first season. 

While the characters are a big reason why stranger things is such a well loved show, Season 2 kinda screwed a lot of them up. Joyce is still the distressed mother (while she has reason to be, she literally doesn't change at a l l after the first season), Mike is kinda just an edgier version of who he was last season, Will doesn't have any character at all (the mind flayer does take him over but thats a slow process in the beginning, he should be more prominent but he isn't and we therefore never get to connect with him like we did with the other characters in the first season), and Eleven...

well Eleven is a child. she has every reason to be disappointed or angry that she cant see mike but she behaves like a toddler. she   - throws tantrums  - breaks windows when she doesn't get what she wants

and yells “i hate you!” at her parental guardian who is just trying to keep her safe from murderous government officials. While her motivations are there and are valid, her behavior is extremely immature, and she definitely devolves after the first season.

whoo that was long. i still like the season but the characters don't really evolve or develop at all after the first season which kinda sucks considering the first season was so good and characters did develop during it, but for some reason they just abruptly stopped. Fortunately, the writing is still decent and the cinematography is still great so its still an enjoyable watch.

SEASON 3: COMMUNISM IS THE REAL ENEMY

okay.

this is probably the worst season of the show (worst for stranger things is still pretty good though), but i still enjoyed watching it more than i enjoyed watching any other part of the show because i was laughing the entire time.

The writing in this season is either amazing or terrible. There are some parts to this show where they’re trying to write a joke but it fails so hard i start laughing. The best example I can think of this is the scene where Billy is trying to convince Mrs. Wheeler to get private lessons from him. He launches into a monologue of how he could “teach her” and starts listing strokes like the sensual man he is 

“freestyle..

breaststroke” *proceeds to eye mrs. wheeler from head to toe*

and the joke made me cringe so hard i fell out of my chair laughing. This is just the example i thought of off the top of my head, but so many scenes have similar writing that makes me cringe hard.

BUT

the actor’s performances in this season are phenomenal. Every actor sells their lines so hard that I enjoy every. single. second of the show even when the writing is dumb. the only times when the writing is actually bothersome is in the serious scenes (like the infamous “new coke” scene which made me shake my head so fast my glasses flew off my head). Apart from those few instances, however, almost every second of the show is enjoyable. This season also fixes the problem with the second season and actually ups the ante this time with the mind flayer which is absolutely, positively, terrifying. The damn thing is literally made out of the melted corpses of the people it infected. This brings another problem into the show, however, which is

too

much

gore.

I should probably start out by saying that in general i don't really like over the top gore in media. A few months ago i tried to watch Kill Bill and got freaked out by the first scene. Regardless of my wimpiness, however, I think that the show begins to rely too much on gore to be scary. Some scenes have people feeling around in a cut open leg, some scenes have people literally melting into chunks of blood and flesh, some have scenes of a guy getting his head shoved into a fan and having his face ripped open. The show tries to put all this off as “horror” but in reality its just something that grosses me out a bit but then i move on. Some scenes have actual scary moments, (especially with the mind flayer in billy’s form), but a lot of the horror in the third season relies on either gore or jumpscares which are still really enjoyable to watch but aren't really scary as they’re intended to be.

I still loved watching season three, but i feel like it shouldn't be gone into as a horror show as the first two could have been. The first few episodes are like a corny teen dramedy with some scary elements, and the last few are literally just slasher 80s camp the whole way through and i'm living for it

anyway this was long winded and dumb. stranger things is a great show watch it just don't expect anything to top the first season

hell yeah abuse tumblr algorithm with hashtags

[i-like-writting-sometimes]

Yesterday (24th of June 2019), I went to bed and, as I sometimes do, I opened the YouTube app to listen to music before going to sleep – I don’t have any downloaded music in my phone because of lack of space so, yeah, I do that. As usual, I scrolled down to see if there was any video or music that interested me at the time, and I found a video named “50 Minutes to Save the World”.

Now, I’ve always been interested in Ambiental issues, and always had some conscience about pollution since I was a kid – the kindergarten I went to made/makes (it’s still running and still conscious of the global issue) sure every kid that went there was educated about global warming, pollution and ways to recycle and reuse stuff.

My grandmother even told me – and I have no memory of this, that’s how young I was at the time – that, once, me and my family were at a café and a man ordered a coffee. In Portugal, where I live, when you ask for a coffee, the waitresses give you a tiny paper bag with sugar so you can add it if you want. Now, my grandmother tells me that, right when I see the man throwing the paper bag to the floor after using it, I pointed to him and said really loud “You don’t do that!!”. With the man looking at me, my mother made me quiet down, afraid that he would get angry or something, but my grandmother noticed that he was very embarrassed instead and ended up picking the paper bag from the floor.

I grew up still worried and conscious about pollution. At the age of what? 7? 8? I remembered being with my dad, in the car, and telling him how idiot the human race could be, as it was killing itself by polluting, because that would increase the global warming and ended up affecting us too. At the age of 11/12 I joined the Ecology Club and, as long as I went to that school, I went to almost every meeting, even going to events with my colleagues (I had no notion of how bad ass that was at my time, actually. I wish the high-school I went to had more publicity for the many clubs it supposedly has).

I never stopped caring about the planet, even when all my colleagues would throw trash to the floor claiming they were “giving a job to the street cleaners”. Hell, I yelled at a friend for throwing trash to the floor and made him pick it up and put it to the plastic bin. Even when nobody around me seemed to care at least as much as I do, I never stopped caring.

Could I do more? Yes, absolutely, and I wish I could simply correct all the things that affect the world in a negative way just with a snap, with one radical change, but the truth is that I don’t have enough capital to do that. Plastic free stuff are still a bit expensive to my family, and seems a bit hard to find at a price that we can afford; and I can’t donate to big organizations because 1) I have trust issues with money, I barely trust myself, honestly; and 2) I usually prefer to have that money so I can help my family with some other things.

But that doesn’t stop me. It’s just like in the movie “The Princess and the Frog” – you gotta work for it to come true, the wish doesn’t come true for itself. And I’m taking baby steps – I bought my own bamboo toothbrush and straws, and I’m planning meals to eat more vegetables so my body will accept a vegetarian eating habit in a less radical and, (in my point of view), more healthy way, giving me time to learn about what I can eat, and how to manage the nutrients and stuff. And I’m getting more conscious about what I consume every day.

And, on top of all that, I’m finally surrounded with people who also care about these things! All my friends are conscious, and although some are more negative than me, we all are trying to take baby steps and change the world. My best friend just bought her metal straws, and another is going to buy her own bamboo toothbrush. We all have our own reusable water bottles and share information about products and ways to recycle and reuse stuff with each other – I’m really proud of them, as you can see, and also proud of me for surrounding myself with these people.

Yet, I know it’s not enough, there’s a lot of stuff I need to change as an individual, and, mostly, actions that need to be made by a collective group. It’s my wish that my actions, and mostly my ideas, will affect society and make the world a better place.

The thing is, I still have a lot of things to learn about this situation.

Even I, who has always cared about the future, the planet and the life in it, who does not want to go to Mars or any place out of this Earth to live, still am not educated enough to have practical ideas to propose to the government, nor do I understand how politics work (I feel like I’m a peasant from medieval times, basically). There’s still people and organizations’ work I need to investigate, crafts that I need to discover and share with my friends, local shops I need to go and consume, propositions and ideas to be written down and discussed and very little time.

So little time, it fucking scares me – I’m not ok, all this situation gives me stress on a daily basis. I was in a Drawing class, me and my friends discussing global warming, when one of them tells me that no matter how many trees we plant they won’t be enough to recover from the damage of the ozone layer. I physically froze, right there. I looked at him in the eyes and, just like I was a child again, I asked him “Really?”. And he confirmed it. I nearly cried in front of my friends because of how hopeless I felt in that moment, while they kept talking and eventually changed topic. I went home after that class completely devastated, depressed.

It took me a few hours to recompose myself and convince me that there were still other things that could be done. I thought that I could still do it in other ways, and still keep that hope with me, every single day.

After watching Amir Zakeri’s video, I felt it physically. My head started hurting, and all I could think was that there was still the possibility of the biggest fear of my 9 years old me coming true: world destruction/world’s ending – and I don’t want that to happen, neither does anybody.

So, hopefully Amir Zakeri didn’t make a video form nothing and neither did I shared it and write all this for nothing! I hope that after reading this, you too will be more aware of what’s happening and starting take baby steps like me. There is a lot of things that can be done!

Starting now, you can share the video that I shared, or this text, or even other kinds of media that will educate you about this issue. Even if you can’t do everything that there is to be done, there are some other people who can, but maybe don’t know about it. So, by sharing information, the possibilities it will reach them, and they will help in ways that you understandably can’t, rise. The more people who know, the more people will start taking actions and choices to help make the world a better place, making easier that others will too!

Also, you should search for activists and organizations and try to support their work in any way possible! The video of Amir Zakeri makes many references about organisations that are working on restoring and protecting the coral reefs. Search them, follow them in your social media platforms and soon, thanks to an algorithm, more accounts of other similar organisations and information on the topic of coral reefs and ecology will start to appear on your explore page.

Learn from whatever you find and start acting! Life is about experiences and learning from that same experiences so you can be better! Start by trying to do small things and slowly you will grow to be affecting the world in a positive way. If many individuals around the world start to do small changes, if we gather all the work of these people it will make one big difference. We don’t need people to have a perfect zero-waste lifestyle, we need everyone, or at least a hell lot of people, to have an imperfect one.

So, start acting in any way you can, and never think that everybody already knows about this. People may have an idea, but it’s only the tip of the iceberg, there’s always more to learn. But also, don’t be mean to people who don’t know/understand this threat. Instead, teach them about it, and they may join you in this cause! I, myself still have a LOT to learn, a LOT of activists to search for (I barely know one or two), but I’m still trying to do the best I can.

[gigilberry-wips]

20 Facts About Charlotte (and family) That The Readers Will Not Be Given In The Story

So here I am trying to organize my thoughts and do some character building in order to answer some questions about my oc that someone was very nice to ask and which I still need to come up with answers for, carry on with your business don’t mind me

(for those who’re sticking around to read this, a warning: this is long as heck)

1. Charlie was known as that kid who’d always be full of questions and giving the adults a run for their money “Why do crocodiles have big noses?” “Where does the moon go?” “Why don’t humans have claws? Aren’t they more useful than fingernails?” “Why do people sleep?” “Why are we supposed to do this? Why can’t we do that?” “Why can’t we ask questions? How would anyone learn things if they don’t ask questions??”

2. In her family she’s closest to her older brother. She doesn’t always share her concerns with him but when she comes to him with her thoughts and questions she trusts his words.

3. One time she read in a book where a meal of grilled cheese and tomato soup was described so delectably that for one entire month she insisted on having that at least once a day (it’s like what happened with me and when I read about the ‘bread and butterflies’ from “Through The Looking Glass” and now if anyone ever offered me a towering stack of heavily buttered toast with an ocean of horrifically sugary milk tea I swear I will devour the whole thing no hesitation). She stopped being so vocally fussed with them after that but she never really got over it. So if, whenever she might see either one of those two (or both!!) in the wilds, be it in person or on television or if she just catches the smell of it, and if you happen to be looking at her already then you just might catch her making The. Biggest. Heart Eyes. Like the love of her life has appeared before her and she is going to run into their arms and both of them will head off into the sunset.

4. Her favorite things to get on Christmas and her birthday are “fun science projects for kids”, or puzzle and strategy games, or books that had riddles, secret codes, recipes, more experiments, and especially made-up languages in them. She’s filled entire notebooks with the languages that she’s learnt from books, from Morse Code to Tolkien elvish, and she can easily recall many of them from memory. She knows a lot of the most common kinds of numerical puzzles and algorithms that have been used, and partially due to that and partially because of how good she is in math she frequently makes computer related jokes about herself.

5. Charlie’s brother is doing an internship at a nearby aerospace museum and planetarium. He’s currently studying for a degree in astronomy and engineering and works as one of the technicians there, and about once a month Charlie’s family goes to visit and have a picnic nearby and spend the day there. Since it’s so close, her brother is able to go from home and usually drops her and her friends off to school in the mornings in his old, beat-up car because he’s a good brother and he loves his sister.

6. One time when Charlotte was little (about 7-8) she was loaned a textbook from her school about famous people that she had to do her homework from. Her brother caught her scribbling in it with a pencil one time and found out that she was replacing all the pronouns of the historical figures in it (Mr. Miss Alexander Graham Bell, he she invented, Mr. Miss Albert Einstein, he she discovered, etc.).

(She didn’t really have the words for it back then but essentially she was doing this because all of these Oh So Important People Of History(TM) Who Did Oh So Important Things(TM) were different from her and she was very strongly aware of that and it made her really, really angry. She thought that if the only thing anyone was ever going to teach her was White Man History(TM) and that’s the only thing that ever existed since the dawn of time and that’s the only thing she’d ever learn then she wasn’t just gonna sit there and swallow that like the rest of her classmates, thank you.)

The next day he bought her a book about famous women in history from all around the world. She read it cover to cover and has kept that book with her to this very day. It’s got pictures like this in it too :D -

[image description] Three women (left to right: Indian, Japanese, and Syrian) who graduated from the Women’s Medical College of Pennsylvania in 1886

This is also one of the reasons why she likes Star Trek so much, and why she wants to learn so many languages.

[Edit: if anyone’s wondering, her brother’s the one who erased the pencil marks from her textbook. He told her since she’s the one who did it then she should be the one to remove it. But she refused to do it and she did not say sorry either. He knew she’d get in trouble if he told his parents or anyone about it, or if it was left alone, so he sat down with an eraser and methodically went through the book with it himself.

Charlie neither offered to help nor stopped him (she could’ve if she’d wanted to, she had cheeto crumbs on her fingers and she could’ve smeared them in the book or poked her brother with them but she didn’t); she just attached herself to his side and quietly glared because that textbook is Enemy #1 and no one should touch it as his hand patiently went through page after page and removed all traces of her vandalism]

7. Charlie loves her hair. When she was little one of her favorite shows was My Little Pony (don’t tell anyone but she still has a soft spot for it) and she wanted to have bright, colorful hair just like the other ponies did. One of her cousins was very fashion savvy and when she told her this, her cousin showed her all the fun hairstyles she could do with her own hair instead. She’s been growing out her hair ever since. Now Charlie and her brother are Long Hair Siblings(TM). :D

8. On the other hand, Charlie despises make up. When she was little she noticed that almost the only people that were on make up advertisements were white women, so in her head she thought that meant those pretty women owned the make up companies, right? Well, she looked it up and learned that the people who really owned the companies were not those women but instead ugly old men and that was when she came to the conclusion that advertisements are all full of lies and not to be trusted (also she learnt later that silicone rubber is used in making water proof mascara and her brain is forever scarred with that knowledge and now so is yours :DD)

9. When she got her first loose tooth she heard about the tooth fairy and how she takes teeth and leaves money. So her natural course of action was to look up the price of human teeth online. Then she took the case up with her father and told him all about her findings and how the tooth fairy was basically scamming everybody and should not be trusted. He found this entertaining enough that the morning after she lost her tooth she found a 2 dollar bill under her pillow. She kept it away safely and once she had enough “tooth money” she bought a whole set of glitter gel pens with it.

10. The number of times Charlie’s gone to a party can be counted on one hand, and that’s only because she was forced to go. One such house she’s frequented is one of her aunt’s and after all these years the only name she knows from there is the cat’s, whose name is Toast but she thought that was boring so in her head she renamed her as Clementine. She hasn’t told anyone else that she’s never learnt anyone else’s name but she has the feeling her brother knows.

11. She loves cats. She loves them so much. She was always such a solemn and serious little girl but the moment she saw a cat it’s like watching a toddler wandering after a butterfly. Abso-lutely adorable. She has these knitted cat socks and 2 cat plushies (one more worn than the other) and when she was 11 her parents let her and her brother adopt an orange kitten and she got to name it Tigger after one of her favorite childhood characters. In her friend group there are so many cat puns surrounding her. So many. (half of them are her own btw)

12. Charlotte is bisexual. I remember reading somewhere that it’s unrealistic to just have one lgbt kid all by their lonesome in any story worth telling and I agreed with that. I’ve also heard about the “disaster bisexual” troupe. In my cast of characters the one that fits it the most is Josie, so me being myself I flipped that troupe and instead made the most calm and collected one the bisexual kid (so instead of a disaster bisexual(TM) what we have is a distinguished bisexual(TM), thank you and good night). It’s not mentioned in the story because this story is told from Laila’s point of view and Charlie hasn’t told anyone about her sexuality, not her friends, not her family, not anyone. She learnt about it earlier than Laila did (when she was 13), but like it’s said in her intro she’s a very cautious and private person and it’ll take her a long time to think about something so personal openly let alone talk about it with anyone. I want to talk about this more in a separate post, and I’ve got a one shot planned that’ll focus on this too.

(Edit: so it turns out Charlie is in fact a bit of a disaster human and when I told her she comes off as smart and polished and good at judging people’s intentions she turned around and told me she also hisses under her breath at things she doesn’t like, lives in her room like it’s one giant nest, and sometimes forgets to eat and i find it too annoying to argue with my strong willed daughter so here we are goddamn)

13. For Charlie, feelings are ... awkward. They’re messy and confusing, and when she’s feeling too many things she needs a lot of alone time to sort through them and understand them. It’s not that she doesn’t feel anything, it’s just that she can’t usually identify what she feels from the whirlwind in her head in any proper way. And when people need comforting she doesn’t feel like she’s the best person for the job. But that’s not going to stop her from trying to help; if one of her friends comes to her with a problem then she’s going to help them find logical solutions to those problems. She knows her strengths and she tries her best to use them.

14. She finds it hard to cry. Even when she’s feeling too many things and she really wants to cry (because she thinks maybe that’ll help her, at least it’s scientifically proven to help) the tears won’t always come. Aside from early childhood, she can count on one hand the number of times she’s cried, and half of them are from when she was exhausted or shocked with sudden feelings. The other times feel random to her and often at odds with each other. (She can’t force herself to cry, she can’t fake her own emotions.)

15. One of the few times she remembers crying was the first time she saw the Aurora Borealis. She saw it in a movie theater, not in person, but to her it was like seeing the real thing. She was little at the time and when she saw it she was just - she was overwhelmed. She was overwhelmed with so many feelings, like happiness and beauty and wonderment. When they came out of the theater and her family saw her still crying they all started freaking out, until she tried to explain it to them (she was really choked up but she tried). She remembered describing it something like, “It’s like seeing music ... Mama, I think I saw music.” she counts this as the one time she expressed her words so artistically she doesn’t know how but she did. it was also one of those rare times she was envious of artistic people for being able to express human emotions so well (there’s that part in The Tale of Despereaux when he said that he “heard honey” when what he’d really heard was music that comes to my mind). Later, she was told what the lights were called and she decided then and there that if she ever had a daughter then she’d name her Aurora (no papa, not from Sleeping Beauty, this is different!). It was also around this time that she really got into learning about space.

16. Charlie’s a night owl. She loves being awake when all the world is asleep. She loves the silence and the clarity she feels in her thoughts when there is no one else around.

17. Charlotte has an “all things pink and glitter” obsession that she never quite grew out of and never really plans to. Her room is pink, her glasses are pink, her stationary is pink, most of her clothes are pink, and her favorite Care Bear and My Little Pony characters are also pink.

18. She got her glasses when she was around 10, and she even got to choose them herself. :D The sad thing was that she was only one of 2 kids in her grade who had glasses and the other one was who she considered to be an annoying prat, but the good news was that at least 3 girls in her year got braces and one of them was nice and called her glasses pretty and also she was the only one who’d done her braces sparkly so there.

19. Most her life she never had close friends. She was always considered too smart and aloof for them. She had her nose stuck in books and she always got the best grades in her year. She was also really good at chess and strategy games and not to brag but she’s even one a few awards for this and this quality was always something that alarmed and frustrated people to no end (read: boys who wanted to prank her and various arrogant, would-be bullies) when she would know all sorts of things about them that they never remembered telling her. What they didn’t know was that she gathered all that information just from observing them and listening to what they said. She’s a strategist and a planner and she delights in knowing more than everybody else, making it so that when she wasn’t purposefully faded into the background, she came off as intimidating and scary, and rightly so. You cross her or try to pull any nonsense around her and she’ll make you regret it.

20. Contrary to what I feel might be predictable for her, it wasn’t Hailey (the friendly and cheerful one) or Josie (the smart and sociable one) that pulled Charlie into Laila’s friend group, but instead it was Laila herself. Charlie might not be good when it comes to feelings, both hers and other people’s, but she’s an excellent judge of character. She doesn’t talk to her peers because she’s categorized them as not being her “type”. She sees them and thinks they’re silly and petty and loud and annoying. She gets impatient with how childish and flighty and apparently short of memory they are, how they haven’t yet decided what they want with their life, how they’re all sooooo fussed about what other people want them to be and how they haven’t made up their minds about who they want to be. Dealing with them is boring and somehow oddly exhausting, so she doesn’t waste her time with them.

She and Laila met through circumstance. And what she immediately got from Laila was that she was someone who was filled with something akin to gentle warmth. She saw someone who didn’t judge or expect things from her. Someone who didn’t raise her hackles or crowd her space, both physically and mentally. She saw a person who didn’t pretend to be something she wasn’t, who didn’t really have anything to hide. Most of all, she saw in Laila someone who went about her day with honesty and good will in her actions towards others, who was genuine and caring. Those are things she’s not often found in other people, no matter their age. It’s something that she’s come to appreciate and respect in the rare, rare instances when she does find it. It took a while, but as she got to know Laila and her other friends better and hung out with them more often, she saw that she found a place where she felt like she could breathe.

[karmies]

Affiliate Marketing on Facebook (2021 Guide)

  If you've been dabbling in the online space for any amount of time, you probably know what affiliate marketing is. If you don't, then you will probably be surprised to find out that it's probably the best way to make money online as a beginner.   That's because you don't need to create your own product. You can literally make thousands of dollars every month by promoting other people's products and make sweet commissions for your troubles.   Although it sounds very easy on paper, affiliate marketing became more and more popular in the last couple of years. Meaning that the competition is not be something that you should neglect.   However, that shouldn't stop you from starting your first affiliate business. With the right training, you have a very good chance of succeeding in this industry and start making passive income (more on that later).   Facebook is one of the most popular platforms for affiliate marketers, simply because they know more about their users than their own families. Think about it: Facebook literally knows everything about you.   They track absolutely every scroll and click you perform on their platform. That's amazing for people like you and me, because we can target the exact people who are the most likely to purchase what we offer them.   As a side note, Facebook ads are my favorite way to make money online and I've been doing it for more than 7 years. If you're asking yourself whether or not you can too, than the answer is yes. Facebook isn't going anywhere and you should at least consider giving it a try.   If that already got you excited, then you're in the right place! By the end of this guide, you will be familiar with affiliate marketing on Facebook, as well as what steps you will need to take in order to start making money as an affiliate.   Affiliate Marketing On Facebook: How To Start   In order to get to the point of making commissions on autopilot, you first need to become familiar with the steps required to get there. There's no reason to rush and spend hundreds and thousands of dollars on your first campaign hoping it will pay off (trust me, I know).   If this is not the first affiliate marketing guide you read, then you are probably familiar with some of the things you are about to read. If not, then you should have an overview of the things you need to do to get started.   Choose a Niche   You've probably heard this a million times, but I will do my duty and tell you again. You need to choose a niche in order to be successful at ANY business.   We all want everyone to be our target market and make as much money as possible. The truth is that you must establish who will be the most interested in the products you recommend.   For example, you can't sell dog training products to people who are interested in fitness. I mean you can do whatever you want, but as you can probably guess, you will not make much money in that specific scenario.   The most popular niches are health, wealth and relationships. However, that doesn't mean that these are the only profitable niches.   Personally, I've had a ton of success in many obscure niches that nobody even thinks about. For example woodworking, dog training, make up, office chairs and so on.   The possibilities are endless and you shouldn't choose a niche just because it seems profitable. With the right training, you can have success in any niche.   - Try choosing a niche that you are a bit interested in. You don't need to absolutely love it or be married with it. It's just that if you choose something that you at least mildly like, you will be more likely to continue and not give up. Sometimes (especially in the beginning) it can be a bit discouraging when you don't see money immediately rolling into your account.   - The niche should have good products. Don't think too much about this, but there should be at least a couple of decent products that you can recommend. If the niche is full of crappy products, then you will have a hard time selling them.   - If there are a lot of people searching for products in that niche, this is a good sign. Traffic is one of the most important aspects of any online business.     Create a Facebook Page   After you choose your niche, the next step you need to take is to create a Facebook page. It should be focused around that niche and you should constantly post new content and promote your offers.   Once your page will start growing, the Facebook algorithm will naturally push your page towards the top. Keep in mind you need to have some sort of engagement from your audience, meaning that people should be active on your page.   You might be tempted to create your own Facebook group. Keep in mind that these don't perform very well when it comes to making sales, so you it's better to stick with pages.   Try creating polls, asking questions and posting nice pictures. You will be surprised how easy it is once you start getting a hang of it.   Don't Spam Your Offers   One of the most common mistakes all beginners make once they start their affiliate business is spamming their links. Keep in mind that this is not the way to grow a legitimate business.   This principle is valid even in the real world. Imagine going into a clothing store and having an employee harassing you to buy a T-shirt every 5 minutes. There's no need to say that you will not be returning to that store anytime soon.   So, yeah. Don't annoy people with your offers. In stead, you should do your best to provide useful content and every once in a while you can post an ad. In my experience, out of every 5 posts, only 1 should be an ad.   However, don't be afraid to experiment and have fun!   Don't Link Directly To An Offer   Another important aspect of affiliate marketing on Facebook is that they will probably ban you if you post directly to an offer. Also, your conversion rates will suffer if you do so.   In stead, you should create a sales funnel or a landing page for your customers. You will be surprised on how well these perform. You will make much more sales, and therefore your business will grow faster.   If you were to start your own affiliate blog and post content over there, you shouldn't worry about rules and regulations too much. However, it will take significantly longer to start getting results as opposed to Facebook.   Remember that Facebook has a ton of rules in regards to affiliate marketing. Try your best not to break them and you will be fine.     Use Facebook Ads To Grow Your Page   As you found out in the beginning of the article, Facebook ads are my favorite way to make money with affiliate marketing. This is simply because you can make money quite fast and once you start getting good at it, you will need to work less and less.   If you get anything out of this guide, remember this: Facebook ads are very quick and profitable in terms of growing an affiliate business. You can pay Mark Zuckerberg to give you traffic so you don't have to wait a century to grow your business.   Keep in mind that you will need to get good at this in order to consistently make money. If you see that your ad doesn't work, just close it and try another one.   If you have a decent budget, I highly recommend investing in Facebook ads. Not only you can be very profitable, but it's also a skill that will stay with you for your entire life.   Do Facebook Ads Allow Affiliate Links?   Yes, Facebook allows people to post their affiliate links in their ads. There has been a lot of misinformation regarding this subject and that's why I decided to address this topic.   You are allowed to post your links in your ads. However, Facebook doesn't suggest you do so if you link to inappropriate pages, such as pornography and other obscure stuff like that (I hope it's not your case).   Also, you shouldn't pour money into a post that contains an ad with your link. I did this before and I got banned. So yeah ... don't do that.   Facebook ads are so profitable that you don't want to make the admins upset and ban you. If you exceedingly post your affiliate links in your ads, you will probably make good money for a short period of time. But I suggest you capture those leads in your email list so that you can sell them more products in the future.   Don't neglect email marketing, as it's one of the best ways to promote your affiliate offers (not better than Facebook ads, but still an option to keep in mind).   Once you capture that email, you will be able to sell them products for as long as they stay on your list. If you pay for Facebook ads, then it makes sense to get something more long term out of it, apart from 1 sale, right?   Facebook Affiliate Marketing Rules   Ask anyone that has been running Facebook ads about the rules and they will immediately get angry. Their rules are so vague that it's irritating.   Sometimes they will suspend your account and you need to email them in order to get it back. Not such a big deal, but it can get quite annoying.   On the other hand, you should always state that a link is an affiliate link if that's the case. There's no need to get into legal trouble.   Again, Facebook rules can get quite annoying, but you shouldn't care too much because the ads are very profitable (if you have the right training).   Final Thoughts   Affiliate marketing is one of the best ways to make money on the internet. And Facebook is a great platform to do it on.   Not only because it can be really profitable, but also because Facebook is not going anywhere. Which means that once you learn how to do it (it will maybe take a couple of weeks or months), you will have transferable skills that will stay with you forever.   If you want to learn how to become a profitable affiliate marketer on Facebook, I highly suggest investing in Robby Blanchard's Commission Hero. It will teach you everything you need to create a profitable affiliate business. You can thank me later. Read the full article

[olgagarmash]

She called out health care misinfo on TikTok. Then, the trolls found her. – NBC News

The video posted to TikTok showed a woman in a blue cardigan and brown medical scrubs dancing to a remix of Wale’s “Lotus Flower Bomb.”

On screen, sandwiched between two sparkle emojis, the woman, who said she was a pharmacy technician, had written, “Most common meds I’ve filled that cause cancer.” She then went on to claim medications like hormonal birth control, cholesterol medications and chemotherapy were cancer causing.

So, Savannah Sparks, another TikTok user who goes by “Rx0rcist,” made her own video, part of what would become an ongoing series debunking medical misinformation on the app.

“My name’s Savannah. I’m a doctor at a pharmacy, and I’m about to absolutely wreck your s—,” Sparks says in the video before launching into a fact-check of the pharmacy technician’s claims.

But Sparks didn’t stop there. She then contacted the woman’s supervisor.

“Her scope of practice doesn’t allow her … to counsel on medications so, especially coming from the realm of pharmacy, which is my wheelhouse, I really went in on that individual and I was like, ‘You really should not be talking about this,'” Sparks said.

Sparks, 31, a Mississippi-based lactation consultant and doctor of pharmacy who is also a mother of a 2-year-old daughter, has become a prolific watchdog on TikTok for those she says are trying to spread misinformation — especially health care workers spreading bogus information about Covid-19.

“In the past, I have been a little more reserved with how aggressive I have gone after these people, but the longer this pandemic went on, and the more and more misinformation we started seeing as health care workers on social media, the less I started caring about my tone and coming across a certain way,” Sparks said.

This has earned her a massive following on TikTok. Her account has more than 467,000 followers and her videos rack in hundreds of thousands — and sometimes millions — of views.

Sparks said she is not only looking for the removal of health care misinformation on the platform, but she also wants accountability.

“Anything that forces somebody to change their way of thinking … it makes them angry,” Sparks said. “So, keeping that in mind, the fact that I’m doing this to so many people, I accept I’m doing exactly what I need to be doing, and I’m exactly where I need to be.”

This approach to calling alleged offenders out has made her the target of online harassment. Her address has been posted on extremist websites, and her inboxes have been flooded with threats of rape and death against both her and her daughter, which, at one point, became so relentless it nearly drove her off the internet.

Misinformation and callouts

Sparks’ most exhaustive callouts are part of a series on her TikTok that she calls “Petty Journal Club with Sav.” She said the videos began as a way to thwart general health care misinformation from spreading on the app, but soon morphed to be more specific as she said she realized some health care workers were not only propagating misinformation about the pandemic, but also teaching their followers how they could get around Covid restrictions.

Using public information and social media, Sparks said she would identify the TikTokers making dubious claims or bragging about skirting rules and contact their employers or, in the most egregious cases, their respective field’s licensing board in an attempt to hold them accountable.

And with TikTok’s algorithm frequently elevating Sparks’ videos to the “For You” page, the platform’s infinite scroll homepage, she continued to draw in even more viewers and followers.

Sparks decides how to handle bad actors on a case-by-case basis, she said, contacting a person privately if she feels their intent is not malicious. If a person makes what she thinks is a major misstep — like a health care worker saying they don’t wear masks outside of work, spreading misinformation about medications or stealing vaccination cards — Sparks said she will share that person’s offending TikTok with her followers, explaining why the person is wrong.

“It’s different for each case depending on how much information I can get on an individual and how egregious their error was online, because some aren’t as bad as others,” Sparks said.

Sparks says one of her first “Petty Journal Club with Sav” videos was the pharmacy technician, who claimed certain medications cause cancer.

When Sparks contacted the woman’s supervisor on Facebook, the supervisor was shocked, she said.

“She was like, ‘Oh, my gosh. I’m ashamed. I can’t believe she’s posting that kind of information,’” Sparks recalled.

Karen North, a professor of digital social media at the University of Southern California’s Annenberg School for Communication and Journalism, said one reason viewers are drawn to this type of content is because it’s like a catharsis for their real-life frustration around rule breakers.

“We all know people who have done things that step over the lines in terms of what we think is right during a pandemic, whether it’s not wearing a mask or being anti-vaxxers or jumping the line to get a vaccine … to the extent we’re frustrated by people we know in our own social circles who are breaking our rules. We can now go online and not only watch someone break a rule but watch someone attack someone for breaking a rule,” North said.

After a public callout on her page, Sparks said, the subject will sometimes go private or delete their various social media accounts.

Sparks says she is meticulous about her work and knows she has a responsibility to do her due diligence first because her callouts could have hundreds of thousands of eyes on them and serious ramifications for the poster.

“Even if they volunteer all that information on their own, linking their social media and where they work, unless I can be pretty certain that what they’re saying is not a joke or what they’re saying does have some malicious intent, I’m not going to push hard because I know that when I go in, I go all in,” she said.

She does, however, recall once getting a detail of a callout wrong. A nurse, whom she had called out, listed a hospital as an employer on her Facebook, which Sparks included in a video about the nurse. The only problem? The nurse no longer worked there and a horde of Sparks’ followers had contacted the facility demanding that person be fired.

“People started calling that hospital and then I reached out to the hospital directly and said, ‘This is what has happened. I’m sorry,’” Sparks said.

The roots of callout culture

Jessa Lingel, an associate professor at the Annenberg School for Communication at the University of Pennsylvania who studies digital culture, said callout culture has a long history on social media, and began as a way for people of color to create accountability around major social issues.

“Cancel culture, callout culture, that really comes from practices on Black Twitter of bringing attention to an issue and saying, hey, this is a thing where we need to align. Whether it’s #MeToo in its early days, that originated on Black Twitter, or whether that’s tied to Black Lives Matter or police brutality. Callout culture originated on Black Twitter,” she said.

Lingel added that callout culture has since evolved from a political tool into a way individuals can get back at one another on social media for real or perceived grievances. This often gives way to someone being labeled a “Karen.”

But Sparks has embraced the Karen moniker when it comes to her TikTok content — and she’s not the only one.

TikToker Aunt Karen, 31, who asked that NBC News not use her real name or location in order to protect her safety, is renowned on the app for calling out those who have been caught engaging in racist behaviors.

“The internet has always been a tool, but now it’s an even bigger tool and it’s the main frame for holding people accountable,” Aunt Karen said.

Behind the scenes, Sparks and Aunt Karen said the people who make content calling out bad behavior on the internet, many of whom are women, have built a network supporting one another, and sometimes work together.

“What I think is great is even though we all call people out, there’s different things that these creators speak out on. Aunt Karen talks a lot about racism and, as [she’s] a woman of color, I can learn a lot from that … Not only do I get to make a friend but I learn a ton from these people,” Sparks said.

While experts say Sparks and Aunt Karen’s callouts — which have collectively drawn millions of views — can provide a counternarrative to those seeking more information, they’re doubtful TikTok vigilantism will change people’s deep-seated views, adding that research into online shaming shows it doesn’t generally bring about significant change.

Related

“Health care workers during Covid have enjoyed a lot of public support generally speaking and so that doesn’t mean mistakes can’t be made and that we shouldn’t pay attention to those mistakes. But, in general, the research on online shaming is not optimistic on whether any of this is going to have much of an impact,” Lingel said.

Research also shows that online shaming is inherently impossible to police and can devolve into abuse, including threats of physical or sexual violence. Moreover, online shaming tends to dehumanize those on the receiving end and can turn a person who has violated a social norm into a target undeserving of empathy in the eyes of an online mob.

Harassment

The subjects of callout culture are not the only ones who have had to pay a price for having the eyes of the internet locked on them.

On March 28, Sparks posted a video announcing she was stepping away from TikTok because of an onslaught of harassment.

She said her address and phone number were posted online, and that her direct messages on Instagram were flooded with death threats directed both at her and her young daughter. Her business pages were bombed with negative reviews. And links to her TikTok account were posted to extremist forum 4chan.

“They posted aerial photos of my mom’s house on 4chan, which they paired next to a video of me and my sister dancing in her backyard to confirm that I was still at her house so they could plan to murder, rape, and kill me,” Sparks said.

Sparks said she had always endured modest backlash for her content, but the harassment ratcheted up in March to the point it became unbearable.

“I was getting probably a hundred [direct messages] a day, just every few minutes in my message requests on Instagram, in comments,” she said, recalling that she was sent messages “saying things like, ‘Kill yourself,’ ‘I’m going to rape you,’ ‘I’m going to rape your daughter,’ Very graphic.”

The wave of ceaseless harassment and threats began, she said, after she posted a video about safety precautions she takes when running and got worse when she began calling out the alleged forged vaccine cards that some health care workers were bragging about on TikTok.

“They went to my Facebook business page, they found my family, they found all my friends and started messaging them. Same thing, just graphic kinds of death threats,” Sparks said.

Then, she said, when her information ended up on 4chan, she said trolls began contacting businesses she affiliates with as a lactation consultant, claiming she was a racist and asking that they no longer do business with her. The attacks continued to escalate until someone posted her phone number and the aerial photo of her mother’s house.

NBC News reviewed nearly 20 of the threats sent to Sparks, some of which were sent by accounts with names like “times_up_savannah,” created solely to harass her.

Related

Sparks eventually filed a complaint with her local sheriff’s office and then made the decision to make her callout videos private and step away from TikTok.

But about two weeks later she returned to the app. She said she feels it’s her “duty to stand up and do the right thing,” emphasizing that she wants to use her platform to be an ally to marginalized voices and to others like Aunt Karen, who are also making callout content on TikTok.

“If I’m not willing to do it, who else would step up to do it?” Sparks said. “… A lot of people say, ‘Well, it’s not a big deal, it’s just TikTok.’ But the things that I talk about are a huge deal. Public health is a huge deal, especially when 500,000 Americans have died from this virus.”

source https://wealthch.com/she-called-out-health-care-misinfo-on-tiktok-then-the-trolls-found-her-nbc-news/

[cosmosogler]

hello friends. i didn’t sleep well last night at all. i’ve felt sick and dizzy and on edge all day.

got an email from my graduate advisor. it says “please come see me asap.” i’m guessing he wants to talk about my classical mechanics grade. but at least i have the less unacceptable quantum grade to show for it... have i proved i can do it if i keep working hard?

probably not.

i dreamed about adventure time. in the dream i recognized it was because the show is ending and i don’t want it to end. 

i think the business man showed up again. i think he played a more prominent role in this one. maybe that’s why i can’t remember it very well at all. i can never remember any interactions i have with that guy and it’s kind of intimidating. 

but, of course, the intimidation is all in my head. the meaning is all in my head. because he is all in my head, because my dreams are all in my head.

he feels like some kind of separate thing though. i don’t like how he’s been lurking and i don’t like that every time i talk to him i can’t remember anything about it except that i MAYBE interacted with him and the strong feeling that i don’t like him or the way he thinks very much.

kinda wish he’d do something already instead of showing up in the background constantly. but maybe he has done something and i just don’t remember it.

and no, he’s not “this man.” that guy is spooky but i’ve never dreamed about him. this guy has a blurry face and angular features and i haven’t been able to remember any good looks i’ve gotten at him.

anyway i showered and drove over to linda and david’s house. they made waffles again. we talked for a long time. i admitted that my grades weren’t very good and told them about my struggles to make my disability accommodations work and the amount of misinformation that gets spread around the physics department. they talked about their careers as teachers. we talked a little bit about politics but i wanted to talk more about my worries about what kind of information i’m getting and what kind of story that tells. the “political divide” has become such that it feels like i’m living on a different planet than people who don’t share my ideology and i wonder if facebook algorithms and google and all that have put me into my own echo box without my noticing. 

the conversation came about because we were talking about our very special racist guest back in october, and the financial repercussions of hosting a “free speech” event that my school had no interest in hosting.

they also showed me a really funny chain email video they’d received about a “millennial” interviewing for a job. i felt, like, my teeth grind in anger as i watched it? i think i was offended? have i ever even felt offended before??

they said it was really well acted and it was satire and all that and then while we were chatting on the couch i noticed linda and david falling back on those caricatures to describe the young people in their lives. but i felt comfortable challenging them on that a little bit? i mean yeah i agree people can be surprisingly unreasonable, but i also think, they can be surprisingly reasonable? and all these weird new trends come from a place of wanting things to change for everyone rather than selfish laziness.

or at least, a lot of them do. that’s my understanding anyway.

then it was literally like 3 pm and i got a call from gramma about going to the arcade with my cousin for dinner. so i had to drive home real quick. my card didn’t work at the gas station and i called mom about it and she got all huffy about not being able to transfer money to my card. i said “no, i called to ask if i could get cash back later.” 

she said “oh.”

i got to talk to an actual gas station attendant for the first time in my life! that was exciting. and by exciting i mean not boring.

i’m starting to realize that my criteria for “not boring” are WAY lower than most other people’s. like i can keep myself entertained in an empty concrete room on the floor for hours just by trying to remember songs or videos. 

maybe it’s because i spent so much time grounded with nothing to do as a kid? getting kicked out of the house and hanging out outside for several hours a day every summer in the 120+ degree heat? not being able to handle physical activity?

laying in a hospital bed for days with only one movie to watch, and then laying around in the recliner for weeks after that while i waited for my arm to start working again?

maybe my life was very different to other kids’. 

anyway, gramma and grampa picked me up and we went to dave and busters. i played some arcade games with my brother and cousin. he made a rape joke and when i expressed disapproval he didn’t make a similar comment again. maybe he just didn’t see another opportunity. maybe he understood i didn’t like it and stopped saying it for the next hour. i dunno.

the food made me REALLY sick for several hours. my brother said his food wasn’t very good either. i felt like garbage the whole ride home. my brother expressed that he likes having me home, when i told him my flight is tomorrow. he took the opportunity to complain about our sister using the tv so i said “well she lives here too. if she didn’t watch the tv then it wouldn’t get watched.” 

he said he wanted it in his room but mom wouldn’t let him, which was not what i was saying, but he moved off of being angry about our sister, so it’sssss a win.

when i got home i played some logic puzzles and drew out more comic stuff. the ideas looked better in my head. i didn’t like how the first one came out, but i think i talked about that quite a bit on the picture i did post. i’ll have to beef up the dialogue and work on my storyboarding there. and maybe write smaller.

i am trying to hit a balance between “this guy is genuinely hurting and feels bad literally all the time” and “that doesn’t make him not an asshole.” i’d like to take a sort of story direction with that. by the time the games take place we see that his entourage does (for the most part) genuinely like him. so i figure over time he would have to re-teach himself to consider other people’s feelings, and he would manage it well enough to keep these guys on board for his “blow up the universe” plan.

while i was trying to get a design down for queen jaydes in that comic there i looked up all the backstory stuff i never found when i played through the game myself. it’s actually... almost exactly what i was planning anyway?? so i’m kind of relieved i only have to make some minor changes to my timeline.

the game plays up the flashbacks as a real “romeo and juliet” quick-flame romance that escalates really quickly but i wanted to portray something a little more laid-back and, like... healthy? like sure they bring each other goofy little gifts all the time and blumiere really throws his life into this relationship (he needs a Project at all times and learning to be kind is not a bad thing to devote himself to) but they also communicate about issues and bring up boundaries? i dunno. i wanted it to be something that you’d really believe affected both of them so much that this is the driving motivator behind the villain of the game.

ehh i could talk about it for a long time. i think about it in the shower and in the car when i don’t have anything else to do and i have to keep myself entertained.

see? i was going somewhere with that long aside earlier!!!

i had a long... well, i talked at oz at length about why i don’t mention to my classmates that i draw, brought on by something i said to linda and david earlier. i think it’s mostly because i feel guilty when i talk about my art. like i only have so much skill in my life and if i devote it to my art and pokemon and music and cinematography and other hobbies then it gets sucked out of physics. and physics is important to me, but it’s hard to express myself through physics. it’s easier to stuff myself into little blue demon men to express my emotions. and i have a lot of emotions.

i know that my physics skills suffer when i go a long time without practicing. that’s... true. i have so much anxiety about it that when i take a break i end up taking a LONG break. too long. it’s so stressful... i’d be so much better at it if i wasn’t so nervous all the time. i know this. 

maybe the next step there is to be comfortable with my hobbies and how they have a useful place in my life alongside physics, without replacing it.

mom always went on in high school about how i was drawing “INSTEAD” of doing schoolwork. maybe that’s where i got the idea from. i dunno.

something good that happened today.......... ehhhh. i need to do some stuff before bed and it’s 11:45. i visited with everyone that i had the opportunity to, this break. i got my ipod set up and more or less squared away. i have to figure out how to remotely deposit a check still, and i need to pack, but that won’t take too long, and it’s not hard.

so i guess i’ll grab my ipod and get to work there. i’ll have to jot down some notes for the comic to make sure i don’t forget what i wanna do. the flight isn’t going to be a good time to work on it considering how self conscious i am... i can catch up on the adventure zone instead. 

[chocobro-hijinks]

Android Companion AU

Lucis is an advanced civilization, the crown city of Insomnia is self sustaining and generally safe, but the limited land with which to build on can barely fit the growing population. You are an independent adult who had landed a dream job in the heart of the city, your parents bid you farewell from their farmhouse just east of Lestallum, and now you are living alone in a very crowded, claustrophobic, and constantly noisy business district.

One day, you find an offer of comfort in your solitary life:

Model: NOCT-1.5 (limited number of units produced):

This model is the cutting-edge technology of all companions available in the market, the be-all end-all royalty of the trade. it is never advertised because very few people can afford it, but you’re a tech nerd and you’ve heard of the legends

It’s usually ridiculously expensive and waaaay out of your range, for some reason, this one is on sharp discount in your local computer shop

the clerk tells you it’s on a discount because it has been taken out of the box by a previous owner and returned, but is in top shape otherwise

it’s a small investment even after the price cut and you’re seriously trying to talk yourself out of it, but the more you look at the android behind the sheer plastic, the more you are entranced by the sharp features and slim design.

a part of you hungers to see what the eyes look like once turned on, and what kinds of apps and functions you can install on such a rare product

you take it home, and the moment you plug it- him in, bright piercing eyes glow red for three seconds, and then mellow out to a soft crystal blue

he doesn’t smile as per programming, he doesn’t greet you and ask you what username you would like to sign in as; he simply asks “where the hell am I now?”

turns out this particular android is defective, he has a tangible fracture in the enamel of his back that can’t be seen under the realistic silicone flesh, you start to believe the creeping suspicion that he has been sold and resold several times before finding his way to you, he has learned to loathe it

his energy depletes fast no matter how long you charge him, sometimes he will not respond to your voice commands even though you are 100% sure he heard, the string running between the balljoints of his hip and knee snaps out of place sometimes, and you have to rewind it back into it’s slot every time

his library is mostly filled with video games and movies and you don’t have the heart to wipe it out, eventually you start taking an interest in them as well, and he teaches you a hilarious card game called King’s Knight which you’re pretty sure is made up

his algorithm slowly adapts to your lifestyle: helping you with the cooking, suggesting places in the city for you to visit together, helping you wind down to sleep at the end of a stressful day

he’s not very fond of the alarm clock app, though, and had repeatedly attempted to delete it

he doesn’t have a lot of domestic functionality, either, you’re not sure if it’s lost with the injury to his crystal core or if it’s just the limitation of a ‘leisure’ model

you find his presence, if unexpectedly somber, comforting. it’s nice to have someone waiting for you at home just to talk to you after a long day

now when you see posts gloating about interacting with this model in some tech exhibit, you thank your lucky stars that you found the ‘defective’ one, because the factory default seems to be a tearfully boring and obnoxious prince-type cliche

Model: MT-Series Line: Argentum:

The Argentum (Silver) line is manufactured to look male, while the Aurum (Gold) line is manufactured to look female.

This series was so heavily advertised and mass produced that you absolutely refused to consider an android companion because of the depravity of it!

The whole MT-Series rubs you the wrong way; a bunch of pretty models programmed to act like stuck up bullies with minimum functionality. They’re made to fill up office hallways and do mundane desk jobs

they’re not even good at it, everything about them is mediocre and you refuse to jump on the hype train, not even for the endless customization features.

it is extremely attractive and, because of the affordable price, there’s at least one in every modern household, doing taxes and planning family finances

one day you are waiting on a care package from your family when you receive a huge shipment box covered in foreign lettering. you take it home, assuming they’ve used whatever cardboard container available that can fit all the vegetables of the season, like always

instead you find a used Argentum model with no clothes, bubble wrap hastily stuffed around the hips (to let pass through customs no doubt), no instructions manual, and three charger cables of varying lengths

you’re sure it’s been sent to the wrong address but there’s no way for you to return it because there’s no shipment label or letter.

you have to ask the machine itself where it came from, you plug it in

this boy turned on like a charm, at first he spoke a foreign language but after hearing you speak for thirty seconds his algorithm realigned itself and he switched to Lucian

He introduced himself with machine name “Prompto!” that can’t be modified without inputting a password in Nifillian, he blinks his shiny inquisitive eyes in wait for your name and beams at you when you tell him

you wanted to return him, you really did, but that smile could melt meteors!

you ask him for a rundown of his operating mission and previous location, he stutters, turns off, then automatically wakes up again

most of his default core programs were replaced with homebrew ones, all his optional apps, previous ownership libraries and all of his geolocation data have been wiped clean

this one knows not a lick of complicated math but can vocalize every top40 hit songs for as long as the battery will last

you’ve heard of people who tinker with the androids, and knowing it’s illegal means if you’re caught with a modified machine you could face a fine you can’t afford

he’s yours now, so you decide to teach him how to blend in so that one day you can take him out to see those chocobos he keeps singing about whenever he’s idling

you give him things to do around the house and bless his whirring core he tries. it’s fun learning how to cook with him, what his culture algorithm has learned about fashion, and what kinds of activities his synthetic muscles have learned before he dropped into your hands

 the one thing he really excels at is taking photographs, and the love that shines in his eyes when he’s taking pictures of you make you wonder if it’s possible for someone to install a soul in a machine

Model: G.Ladio Line: 飴.citia (Sweet Line):

this one is straight up advertised as a “Lover Companion”, and most series don’t even come with a shirt

the most slandered model in the Android Companion community, praised only for it’s lengthy charge time and sturdy assembly, it’s nearly impossible to damage this one without intending to

you have no need for a lover android, you tell your friends over coffee, if you wanted a jackhammer you’d get one from the hardware store for half the price

so of course your friends prank you by getting you a G.Ladio unit for your birthday

you don’t realize what they’ve done until you’re opening presents the next day and BAM! topless muscle man unfurls from the Styrofoam packaging to engulf you in a tight warm embrace

it takes you a minute to figure out how to cancel the command your friends have set up to switch to idle mode, by then he’s on top of you with both hands up your shirt and a very real mouth on your neck

you’re angry and embarrassed and flustered, one button press away from chewing out all your asshole friends in one conference call, but the warm eyes and soft smile that look back at you persuade you otherwise

his set up process is super simple, all you have to do is input your name and choose an intensity setting

that’s literally what it was called: level of intensity

you sweat nervously and decide to have it on the lowest setting, while searching for the instruction’s manual hoping there’s an intensity equal to ‘off’ or ‘not yet ready for the whole concept tbh’

thankfully you have something big enough to cover the tattoo your friends ordered with the purchase (your least favorite bird: the crow)

now you can start your day without being distracted by realistic silicone man titties; self-heating-silicone if your first encounter was any indication

low intensity Gladio is surprisingly pleasant, he comes with romance novel apps to recite for you, a warm rumbling voice bank to lull you to sleep, he’s waterproof, heatproof, and knows several party games

he waters your plants when you forget to do it according to a botanic encyclopedia he has installed by default (it’s to select flowers on your dates, but this works better for your succulents and lilies) 

you find it nice to have someone to warm your toes on in bed, someone to enjoy casual cuddling while watching a movie, someone you can program to kiss your neck juuuust right, someone who never tires of you when you’re overly snappy and moody after a bad day

your friends tease you about ‘how are you liking your overpriced jackhammer’, and you laugh at their blanching faces when you tell them you’re already on the highest level of your Gladio and is considering ordering a new drill bit

Model: Scientia Ign-1S:

This one isn’t nearly as advertised as the others, it is manufactured by a highly specialized company for very specific corporate purposes

the only reason you even know it exists is because it is necessary to have one in your office branch to communicate with the other units around the company building

you and your coworkers consider it part of the office furniture, it’s just always there, idling in the background

It has very basic social apps, there is an admin lock on it’s learning curve so you can’t teach it to converse with you, it watches everything silently, recording, seething

One morning you show up to work and there’s security everywhere, there has been a break-in and the android was sabotaged in an attempt to break the encryption

the camera spheres in the eye sockets were ripped out with a crowbar, damaging the satin silicone finish of the face assembly and shattering the glass orb inside a socket beyond repair

one hand is officially lost, a leg was ripped and used to smash into the glass window overlooking the office of your superior

one of the many crystal cores lay dim in it’s exposed chest, having self-destructed as a tampering defense mechanism

a coworker jokes that now your office has a free punching bag

the technician announces that any sensitive data had been completely wiped and is ready for repairs, but your office refuses to fix the machine because it’ll be cheaper to just buy a new one under a different insurance contract, they order a disposal

at the end of the day you find a limb sticking out of the dumpster behind your workplace and yank it out, it is attached to the damaged Scientia android now missing it’s wig and some internal wiring

you’re virtually broke and absolutely cannot afford repairs for such a limited model, but at this point leaving him behind feels like leaving a human coworker to fend for themselves, robot or not

it takes you months of research and the hunting down of parts, of learning how to weld wires and stitch silicon flesh; eventually you end up with a somewhat functioning Scientia model without breaking the bank!

sure one socket is permanently closed to prevent the constant shedding of glass and enamel shards, there are cuts and spots on it’s face that cannot be restored without reskinning his whole head, but at least the new core you scored in an online bid comes with some fancy custom tools!

now he has a massive concept-generator, a library of battle animation files that control the skeletal rig, an advanced linguistic database so he never runs out of words to say, and some strange fashion-modeling script (it installed itself!)

you also find an extensive voice bank to replace the one that had been locked back in the office, this one only comes in an unusual Tenebrean Accent (no wonder it was free...)

you put on his new hair last, a fancy pompador you got off a cosplay site, his delicate face pulls into a soft smile

he’s been watching you, learning you, for over a year as you patched him up and sowed him back together. He knows what foods you like, what music you listen to when you cry, what breeds of dogs please you the most; he offers all of it to you when he obtains functioning limbs

you find yourself eager to go home to your imperfect housemate who looks at you like you’re his sun and stars, and there’s very little you can do to keep from falling in love with his every smile

he is very protective of you, and you feel safe walking with him in a crowded city where not many people recognize him as a machine

you become protective of him, too, and remove the admin limitations on those battle commands so nobody can ever hurt him again

(there’s not enough android companion stories out there, let me live)

as always, these head canons can be freely used for fics, RPs, art, whatever, change what you want and have fun with it!

[careergrowthblog]

Let’s get radical on Ofsted reform. Power:reliability:impact ratio is wrong.

I think it is time for a very significant review of the role of Ofsted, the nature of inspection and the whole accountability machinery for schools in England.  I have a lot of time and respect for Amanda Spielman and I’m writing this hoping she will read it at some point.  I’m sure that much of what follows is easier said than done, but I would like to suggest that we should be exploring Ofsted reform at a much more radical level than the current reform process would suggest is on the table.

I am encouraged by the debate about deeper reform that seems to be starting to gather some traction. Here are some examples:

An excellent article by ASCL’s inspection expert Stephen Rollett : It could be time for Ofsted to stop passing judgement

Rebecca Allen’s recent speech about education reform and trusting teachers.

Stephen Tierney’s August post: Time to seriously question Ofsted.

I recently re-read my 2013 post Accountability We Can Trust – written way before my personal experience of being crushed by the machine.  It is still largely true even if graded lessons have officially ceased to feature in inspections.

Essentially my argument is this:

Ofsted inspections and DFE performance measures are not sufficiently reliable to justify the weight that is placed on the judgements that are made given that a) educational outcomes are not rising within the system b) schools are driven towards perverse short-term behaviours around curriculum and c) there are unacceptable and disproportionately damaging consequences from negative judgements for schools and individuals.

If it were the case that, as a direct result of our current inspection regime and performance culture, we had a world leading education system and teaching was booming as a graduate profession of choice, it would be possible to make a case for keeping it as it is.  However, that isn’t where we are.  In fact my contention is that the current regime is making things worse, not better. It’s like an enzyme or catalyst that’s been heated beyond its optimum temperature: things are starting to break up instead of working better.

I don’t want to dwell on this too much but it has to be restated that the negative consequences for poor judgements are massive. Read Louise Tickle’s recent article about Headteachers being ‘sacked and gagged’ in the Guardian.  This is the reality of our system.  Good people are spat out by the accountability machine in way that is completely disproportionate.  As I’ve said before, it’s pretty f**ked up that our system does this to people – for no net gain.  How long is the line of people queuing up to become Headteachers? Oh wait…. there’s no queue? Oh! And, for me, the thing that makes me the most angry about Ofsted is that there is no official acknowledgement of their role in creating these conditions.

An aspect of the accountability culture is that school leaders are driven to make curriculum decisions that are not supported by sound educational principles. Headteachers have a gun to their head on outcomes and a gun to their stomach on curriculum breadth.  Rock vs Hard Place.   Progress 8 is the latest incarnation of data delusion to infect our system and this is one of the major forces driving schools towards a three-year KS4 with very narrow options models. Nobody anywhere has decided to do that on principle – in government or in schools. It’s simply an outcome of accountability pressure.  I’ve discussed P8 endlessly elsewhere.  It’s a zero-sum arbitrary measure built on an unreliable incomplete KS2 baseline and GCSE outcomes that themselves have a virtual zero-sum foundation: 30% of students must ‘fail’ and, given the grade-inflation freeze, any improvement in school A over here must be matched by a decline in school B over there.

Progress 8 might be a useful technical data aggregation tool to provide leaders with information for evaluating progress across a school but it has no business serving as the main outcome measure at a national level. It simply isn’t robust enough.  A 0.2 school is not inherently better than a -0.1 school.  There are too many variables.  But go shout at the hills. Nobody is listening; even Lead Inspectors do not understand it. Protest = excuse-making (and you’d be wasting your time making an official complaint.)

And then there’s the reliability question.  It’s still the case that no secondary school inspection processes have been subjected to any form of reliability trial. Incredible really.   Daniel Muijs’ appointment is good news but boy does he have his work cut out.  I would argue that every single element is massively flawed. Interviews with leaders, lesson observations, book scrutinies – the whole lot.  An Ofsted grade is essentially a giant subjective punt informed by layer upon layer of bias and selective interpretation of data which, in itself, is hugely complex, flawed and variable.  I have heard so many tales of the horse-trading that goes on as inspection teams try to navigate their way through the framework to reach a plausible sounding final outcome.  Good with Outstanding Features. On the cusp of Good and RI.  Borderline Inadequate.  The top end of Good. A secure Outstanding.  It’s nonsense — isn’t it?

I have made this case repeatedly:  it takes leaders, governors and school improvement professionals weeks and months to fully understand the detail of the quality issues in a school. If you look at how many lines of enquiry are embedded in the inspection framework – safeguarding, SEND, top-end challenge, pupil premium, curriculum, behaviour, leadership, teaching, assessment, performance management, numerous other compliance issues – it is simply utterly, utterly preposterous that this can all be meaningfully, accurately and reliably evaluated in a one or two day visit by a couple of inspectors running around like blue-arsed flies.  (Which is how it feels to them – so I’ve heard.)  “Oh, you can tell by lunchtime whether or not it’s a good school.”. Really? REALLY??

The argument is often made that parents like the grades and that they need good, simple and reliable information about their child’s school.  But they are not getting that. There will be ‘Outstanding’ schools all over the country that are ‘worse’ than schools that are ‘Good’.  There are RI schools that are better than some Good Schools.  It will just be that different teams made different subjective judgements, snatched from all their rushed meetings, lesson fly-throughs and book grazings on different days and the various randomnesses in their minds on those days fell in a certain pattern.

School A:  P8 = 0.65 Good.

School B. P8 = -0.11 Outstanding

School C P8 = 0.44 Outstanding

School D P8 = -0.33 RI.

All of this is delusional misinformation.  All of this has to go.  The appalling cult of Outstanding that has grown in the country is simply ludicrous. I know heads who have had massive mental health issues simply around whether their school falls on the right side of the Good/Outstanding divide -because they perceive the stakes to be so high.  The competitive rat-race behaviours around school badging and promotion are ugly -disgraceful at times.   I once had an email from a Head who had ‘Leadership Ofsted Rated Outstanding’ in her email sign-off. Nauseating.  The next year, her results dropped 20%.  Awkward.  What kind of system creates that culture? Not a healthy one.

So, what’s the alternative?

I’ve got some extremely radical suggestions but, for now, I just want to suggest changes that are plausible and justifiable within our current system:

First of all, follow the Heads’ Roundtable/Stephen Tierney idea of taking safeguarding out of the standards inspections and do them separately. It’s too important and should be done annually by specialists.

Remove all the grades. They are simply too unreliable to sustain.

Abolish Progress 8 as a performance measure and relegate it to the place it belongs as a school management tool.  It’s too flawed as it stands.

Publish an annual data report that does not contain any false comparisons or made-up algorithmic constructs.  Attainment data by subjects, multiple benchmarks, prior attainment data presented in profiles, not averages – detail over simplicity; truth over falsehood.

Publish inspection reports informed by multiple visits including at least one person who has known a school over time.  Reports should focus on key strengths and key areas for improvement written in a language that conveys the strengths and weaknesses in an honest and meaningful manner, including the appropriate degree of complexity where patterns are unclear. This could include suggested timeframes for improvement in certain areas where the issues are significant.

Create a separate process for schools showing chronic weaknesses.  This should include a period of purdah to allow for rapid responses to significant areas of concern prior to reports being published.   Schools need to have the opportunity to make radical changes in response to issues without being exposed to public scrutiny. I envisage something like a three-month rapid response window – not longer, as this would be counterproductive in terms of impact.

My view is that these changes would not be any softer or less rigorous.  But they would be more humane, more accurate and more sustainable.  And if that is true, or even close to being true, surely they should be given serious consideration.  The question is whether the people with the power to make the change have too much invested in justifying the current system to allow themselves to contemplate reversing years of policy.  It’s got to be worth a look though hasn’t it?

    Let’s get radical on Ofsted reform. Power:reliability:impact ratio is wrong. published first on http://ift.tt/2uVElOo

[littlemarketingproject]

Marketing Machines: Is Machine Learning Helping Marketers or Making Us Obsolete?

Hollywood paints a grim picture of a future populated by intelligent machines. Terminator, 2001: A Space Odyssey, The Matrix and countless other films show us that machines are angry, they’re evil and — if given the opportunity — they will not hesitate to overthrow the human race.

Films like these serve as cautionary tales about what could happen if machines gain consciousness (or some semblance of). But in order for that to happen humans need to teach machines to think for themselves. This may sound like science fiction but it’s an actual discipline known as machine learning.

The machines are coming. But fear not — they could help you become a better marketer. Image via Shutterstock.

Still in its infancy, machine learning systems are being applied to everything from filtering spam emails, to suggesting the next series to binge-watch and even matching up folks looking for love.

For digital marketers, machine learning may be especially helpful in getting products or services in front of the right prospects, rather than blanket-marketing to everyone and adding to the constant noise that is modern advertising. Machine learning will also be key to predicting customer churn and attribution: two thorns in many digital marketers’ sides.

Despite machine learning’s positive impact on the digital marketing field, there are questions about job security and ethics that cannot be swept under the rug. Will marketing become so automated that professional marketers become obsolete? Is there potential for machine learning systems to do harm, whether by targeting vulnerable prospects or manipulating people’s emotions?

These aren’t just rhetorical questions. They get to the heart of what the future of marketing will look like — and what role marketers will play in it.

What is Machine Learning?

Machine learning is a complicated subject, involving advanced math, code and overwhelming amounts of data. Luckily, Tommy Levi, Director of Data Science at Unbounce, has a PhD in Theoretical Physics. He distills machine learning down to its simplest definition:

You can think of machine learning as using a computer or mathematics to make predictions or see patterns in data. At the end of the day, you’re really just trying to either predict something or see patterns, and then you’re just using the fact that a computer is really fast at calculating.

You may not know it, but you likely interact with machine learning systems on a daily basis. Have you ever been sucked into a Netflix wormhole prompted by recommended titles? Or used Facebook’s facial recognition tool when uploading and tagging an image? These are both examples of machine learning in action. They use the data you input (by rating shows, tagging friends, etc.) to produce better and more accurate suggestions over time.

Other examples of machine learning include spell check, spam filtering… even internet dating — yes, machine learning has made its way into the love lives of many, matching up singles using complicated algorithms that take into consideration personality traits and interests.

Machine learning may be helpful in getting products or services in front of the right prospects. Click To Tweet

How Machine Learning Works

While it may seem like witchcraft to the layperson, running in the background of every machine learning system we encounter is a human-built machine that would have gone through countless iterations to develop.

Facebook’s facial recognition tool, which can recognize your face with 98% accuracy, took several years of research and development to produce what is regarded as cutting-edge machine learning.

So how exactly does machine learning work? Spoiler alert: it’s complicated. So without going into too much detail, here’s an introduction to machine learning, starting with the two basic techniques.

Supervised learning

Supervised learning systems rely upon humans to label the incoming data — at least to begin with — in order for the systems to better predict how to classify future input data.

Gmail’s spam filter is a great example of this. When you label incoming mail as either spam or not spam, you’re not only cleaning up your inbox, you’re also training Gmail’s filter (a machine learning system) to identify what you consider to be spam (or not spam) in the future.

Unsupervised learning

Unsupervised learning systems use unlabeled incoming data, which is then organized into clusters based on similarities and differences in the data. Whereas supervised learning relies upon environmental feedback, unsupervised learning has no environmental feedback. Instead, data scientists will often use a reward/punishment system to indicate success or failure.

According to Tommy, this type of machine learning can be likened to the relationship between a parent and a young child. When a child does something positive they’re rewarded. Likewise, when “[a machine] gets it right — like it makes a good prediction — you kind of give it a little pat on the back and you say good job.”

Like any child (or person for that matter), the system ends up trying to maximize the positive reinforcement, thus getting better and better at predicting.

The Power of Machine Learning

A lot of what machine learning can do is yet to be explored, but the main benefit is its ability to wade through and sort data far more quickly and efficiently than any human could, no matter how clever.

Tommy is currently experimenting with an unsupervised learning system that clusters landing pages with similar features. Whereas one person could go through a few hundred pages in a day, this model can run through 300,000 pages in 20 minutes.

How do your landing page conversion rates compare against your industry competitors?

We analyzed the behavior of 74,551,421 visitors to 64,284 lead generation landing pages. Now we want to share average industry conversion rates with you in the Unbounce Conversion Benchmark Report.

hbspt.forms.create({ css: ”, portalId: ‘722360’, formId: ‘6d845d0c-65c4-401b-98f2-64aed5474154’, target: ‘#cta-hb’, onFormReady: function() { if (navigator.appVersion.indexOf(‘Win’)!=-1) { $(‘.hs-form select’).click(function(event) { $(‘.hs-form select .hs-input option’).prop(‘disabled’, false);}); } } });

input.hs-button.primary.large { font-size: 15px !important;

}

.blog-cta-side-image img{ max-width: 370px !important; margin-left: -122px !important;} .single-post .section-lib-post .lib-container .entry-content div.blog-cta-form-section .blog-cta-content-with-image .hs-form .hs-button { padding: 10px 15px; }

By entering your email you’ll receive other resources to help you improve your conversion rates.

The advantage is not just speed, it’s also retention and pattern recognition. Tommy explains:

To go through that many pages and see those patterns and hold it all in memory and be able to balance that — that’s where the power is.

For some marketers, this raises a troubling question: If machine learning systems solve problems by finding patterns that we can’t see, does this mean that marketers should be worried about job security?

The answer is more nuanced than a simple yes or no.

Machine Learning and the Digital Marketer

As data becomes the foundation for more and more marketing decisions, digital marketers have been tasked with sorting through an unprecedented amount of data.

This process usually involves hours of digging through analytics, collecting data points from marketing campaigns that span several months. And while focusing on data analysis and post-mortems is incredibly valuable, doing so takes a significant amount of time and resources away from future marketing initiatives.

As advancements in technology scale exponentially, the divide between teams that do and those that don’t will become more apparent. Those that don’t evolve will stumble and those that embrace data will grow — this is where machine learning can help.

Marketers that don’t embrace data will fumble. Those that do will grow — ML can help. Click To Tweet

That being said, machine learning isn’t something digital marketers can implement themselves after reading a quick tutorial. It’s more comparable to having a Ferrari in your driveway when you don’t know how to drive standard… or maybe you can’t even drive at all.

Until the day when implementing a machine learning system is just a YouTube video away, digital marketers could benefit from keeping a close eye on the companies that are incorporating machine learning into their products, and assessing whether they can help with their department’s pain points.

So how are marketers currently implementing machine learning to make decisions based on data rather than gut instinct? There are many niches in marketing that are becoming more automated. Here are a few that stand out.

Lead scoring and machine learning

Lead scoring is a system that allows marketers to gauge whether a prospect is a qualified lead and thus worth pursuing. Once marketing and sales teams agree on the definition of a “qualified lead,” they can begin assigning values to different qualified lead indicators, such as job title, company size and even interaction with specific content.

These indicators paint a more holistic picture of a lead’s level of interest, beyond just a form submission typically associated with lead generation content like ebooks. And automating lead scoring takes the pressure off marketers having to qualify prospects via long forms, freeing them up to work on other marketing initiatives.

Once the leads have reached the “qualified” threshold, sales associates can then focus their efforts on those prospects — ultimately spending their time and money where it matters most.

Content marketing and copywriting

Machine learning models can analyze data points beyond just numbers — including words on your website, landing page or PPC ads. Machine learning systems can find patterns in language and detect words that elicit the most clicks or engagement.

Is emotional copywriting on your landing page effective in your industry?

We used machine learning to help create the Unbounce Conversion Benchmark Report, which shares insights on how different aspects of page copy correspond to conversion rates across 10 industries.

hbspt.forms.create({ css: ”, portalId: ‘722360’, formId: ‘6d845d0c-65c4-401b-98f2-64aed5474154’, target: ‘#second’, onFormReady: function() { if (navigator.appVersion.indexOf(‘Win’)!=-1) { $(‘.hs-form select’).click(function(event) { $(‘.hs-form select .hs-input option’).prop(‘disabled’, false);}); } } });

input.hs-button.primary.large { font-size: 15px !important;

}

.blog-cta-side-image img{ max-width: 370px !important; margin-left: -122px !important;}

By entering your email you’ll receive other resources to help you improve your conversion rates.

But can a machine write persuasive copy? Maybe, actually.

A New York-based startup called Persado offers a “cognitive content platform” that uses math, data, natural language processing, emotional language data and machine learning systems to serve the best copy and images to spur prospects into action. It does this by analyzing all the language data each client has ever interacted with and serving future prospects with the best possible words or phrases. An A/B test could never achieve this at the same scale.

Think this is a joke? With over $65 million in venture capital and a reported average conversion rate uplift of 49.5% across 4,000 campaigns, Persado’s business model is no laughing matter.

Still, there is no replacement for a supremely personalized piece of content delivered straight to your client’s inbox — an honest call to action from one human to another.

Recently Unbounce’s Director of Campaign Strategy, Corey Dilley, sent an email to our customers. It had no sales pitch, no call to action button. It was just Corey reaching out and saying, “Hey.”

Corey’s email had an open rate of 41.42%, and he received around 80 personal responses. Not bad for an email written by a human!

Sometimes it’s actions — like clicks and conversions — you want to elicit from customers. Other times the goal is to build rapport. In some cases we should let the machines do the work, but it’s up to the humans to keep the content, well, human.

There is no replacement for personalized content and an honest ask from one human to another. Click To Tweet

Machine learning for churn prediction

In the SaaS industry, churn is a measure of the percentage of customers who cancel their recurring revenue subscriptions. According to Tommy, churn tells a story about “how your customers behave and feel. It’s giving a voice to the customers that we don’t have time or the ability to talk to.”

Self-reporting methods such as polls and surveys are another good way to give a voice to these customers. But they’re not always scalable — large data sets can be hard for humans to analyze and derive meaning from.

Self-reporting methods can also skew your results. Tommy explains:

The problem with things like surveys and popups is that they’re only going to tell you what you’ve asked about, and the type of people that answer surveys are already a biased set.

Machine learning systems, on the other hand, can digest a larger number of data points, and with far less bias. Ideally the data is going to reveal what marketing efforts are working, thus leading to reduced churn and helping to move customers down the funnel.

This is highly relevant for SaaS companies, whose customers often sign up for trials before purchasing the product. Once someone starts a trial, the marketing department will start sending them content in order to nurture them into adopting the service and become engaged.

Churn models can help a marketing team determine which pieces of content lead to negative or positive encounters — information that can inform and guide the optimization process.

Ethical Implications of Machine Learning in Marketing

We hinted at the ethical implications of machine learning in marketing, but it deserves its own discussion (heck, it deserves its own book). The truth is, machine learning systems have the potential to cause legitimate harm.

According to Carl Schmidt, Co-Founder and Chief Technology Officer at Unbounce:

Where we are really going to run into ethical issues is with extreme personalization. We’re going to teach machines how to be the ultimate salespeople, and they’re not going to care about whether you have a compulsive personality… They’re just going to care about success.

This could mean targeting someone in rehab with alcohol ads, or someone with a gambling problem with a trip to Las Vegas. The machine learning system will make the correlation, based on the person’s internet activity, and it’s going to exploit that.

Another dilemma we run into is with marketing aimed at affecting people’s emotions. Sure copywriters often tap into emotions in order to get a desired response, but there’s a fine line between making people feel things and emotional manipulation, as Facebook discovered in an infamous experiment.

If you aren’t familiar with the experiment, here’s the abridged version: Facebook researchers adapted word count software to manipulate the News Feeds of 689,003 users to determine whether their emotional state could be altered if they saw fewer positive posts or fewer negative posts in their feeds.

Posts were deemed either positive or negative if they contained at least one positive or negative word. Because researchers never saw the status updates (the machine learning system did the filtering) technically it fell within Facebook’s Data Use Policy.

However, public reaction to the Facebook experiment was generally pretty scathing. While some came to the defense of Facebook, many criticized the company for breaching ethical guidelines for informed consent.

In the end, Facebook admitted they could have done better. And one good thing did come out of the experiment: It now serves as a benchmark for when machine learning goes too far, and as a reminder for marketers to continually gut-check themselves.

For Carl, it comes down to intent:

If I’m Facebook, I might be worried that if we don’t do anything about the pacing and style of content, and we’re inadvertently presenting content that could be reacted to negatively, especially to vulnerable people, then we would want to actively understand that mechanism and do something about it.

While we may not yet have a concrete code of conduct around machine learning, moving forward with good intentions and a commitment to do no harm is a good place to start.

The Human Side of Machine Learning

Ethical issues aside, the rise of machines often implies the fall of humans. But it doesn’t have to be one or the other.

“You want machines to do the mundane stuff and the humans to do the creative stuff,” Carl says. He continues:

Computers are still not creative. They can’t think on their own, and they generally can’t delight you very much. We are going to get to a point where you could probably generate highly personal onboarding content by a machine. But it [will have] no soul.

That’s where the human aspect comes in. With creativity and wordsmithing. With live customer support. Heck, it takes some pretty creative data people to come up with an algorithm that recognizes faces with 98% accuracy.

Imagine a world where rather than getting 15 spam emails a day, you get just one with exactly the content you would otherwise be searching for — content written by a human, but served to you by a machine learning system.

While pop culture may say otherwise, the future of marketing isn’t about humans (or rather, marketers) versus machines. It’s about marketers using machines to get amazing results — for their customers and their company.

Machine learning systems may have an edge when it comes to data sorting, but they’re missing many of the things that make exceptional marketing experiences: empathy, compassion and a true understanding of the human experience.

Editor’s note: This article originally appeared in The Split, a digital magazine by Unbounce.

Marketing Machines: Is Machine Learning Helping Marketers or Making Us Obsolete? syndicated from https://unbounce.com

[maxslogic25]

Marketing Machines: Is Machine Learning Helping Marketers or Making Us Obsolete?

Hollywood paints a grim picture of a future populated by intelligent machines. Terminator, 2001: A Space Odyssey, The Matrix and countless other films show us that machines are angry, they’re evil and — if given the opportunity — they will not hesitate to overthrow the human race.

Films like these serve as cautionary tales about what could happen if machines gain consciousness (or some semblance of). But in order for that to happen humans need to teach machines to think for themselves. This may sound like science fiction but it’s an actual discipline known as machine learning.

The machines are coming. But fear not — they could help you become a better marketer. Image via Shutterstock.

Still in its infancy, machine learning systems are being applied to everything from filtering spam emails, to suggesting the next series to binge-watch and even matching up folks looking for love.

For digital marketers, machine learning may be especially helpful in getting products or services in front of the right prospects, rather than blanket-marketing to everyone and adding to the constant noise that is modern advertising. Machine learning will also be key to predicting customer churn and attribution: two thorns in many digital marketers’ sides.

Despite machine learning’s positive impact on the digital marketing field, there are questions about job security and ethics that cannot be swept under the rug. Will marketing become so automated that professional marketers become obsolete? Is there potential for machine learning systems to do harm, whether by targeting vulnerable prospects or manipulating people’s emotions?

These aren’t just rhetorical questions. They get to the heart of what the future of marketing will look like — and what role marketers will play in it.

What is Machine Learning?

Machine learning is a complicated subject, involving advanced math, code and overwhelming amounts of data. Luckily, Tommy Levi, Director of Data Science at Unbounce, has a PhD in Theoretical Physics. He distills machine learning down to its simplest definition:

You can think of machine learning as using a computer or mathematics to make predictions or see patterns in data. At the end of the day, you’re really just trying to either predict something or see patterns, and then you’re just using the fact that a computer is really fast at calculating.

You may not know it, but you likely interact with machine learning systems on a daily basis. Have you ever been sucked into a Netflix wormhole prompted by recommended titles? Or used Facebook’s facial recognition tool when uploading and tagging an image? These are both examples of machine learning in action. They use the data you input (by rating shows, tagging friends, etc.) to produce better and more accurate suggestions over time.

Other examples of machine learning include spell check, spam filtering… even internet dating — yes, machine learning has made its way into the love lives of many, matching up singles using complicated algorithms that take into consideration personality traits and interests.

Machine learning may be helpful in getting products or services in front of the right prospects. Click To Tweet

How Machine Learning Works

While it may seem like witchcraft to the layperson, running in the background of every machine learning system we encounter is a human-built machine that would have gone through countless iterations to develop.

Facebook’s facial recognition tool, which can recognize your face with 98% accuracy, took several years of research and development to produce what is regarded as cutting-edge machine learning.

So how exactly does machine learning work? Spoiler alert: it’s complicated. So without going into too much detail, here’s an introduction to machine learning, starting with the two basic techniques.

Supervised learning

Supervised learning systems rely upon humans to label the incoming data — at least to begin with — in order for the systems to better predict how to classify future input data.

Gmail’s spam filter is a great example of this. When you label incoming mail as either spam or not spam, you’re not only cleaning up your inbox, you’re also training Gmail’s filter (a machine learning system) to identify what you consider to be spam (or not spam) in the future.

Unsupervised learning

Unsupervised learning systems use unlabeled incoming data, which is then organized into clusters based on similarities and differences in the data. Whereas supervised learning relies upon environmental feedback, unsupervised learning has no environmental feedback. Instead, data scientists will often use a reward/punishment system to indicate success or failure.

According to Tommy, this type of machine learning can be likened to the relationship between a parent and a young child. When a child does something positive they’re rewarded. Likewise, when “[a machine] gets it right — like it makes a good prediction — you kind of give it a little pat on the back and you say good job.”

Like any child (or person for that matter), the system ends up trying to maximize the positive reinforcement, thus getting better and better at predicting.

The Power of Machine Learning

A lot of what machine learning can do is yet to be explored, but the main benefit is its ability to wade through and sort data far more quickly and efficiently than any human could, no matter how clever.

Tommy is currently experimenting with an unsupervised learning system that clusters landing pages with similar features. Whereas one person could go through a few hundred pages in a day, this model can run through 300,000 pages in 20 minutes.

How do your landing page conversion rates compare against your industry competitors?

We analyzed the behavior of 74,551,421 visitors to 64,284 lead generation landing pages. Now we want to share average industry conversion rates with you in the Unbounce Conversion Benchmark Report.

By entering your email you'll receive other resources to help you improve your conversion rates.

The advantage is not just speed, it’s also retention and pattern recognition. Tommy explains:

To go through that many pages and see those patterns and hold it all in memory and be able to balance that — that’s where the power is.

For some marketers, this raises a troubling question: If machine learning systems solve problems by finding patterns that we can’t see, does this mean that marketers should be worried about job security?

The answer is more nuanced than a simple yes or no.

Machine Learning and the Digital Marketer

As data becomes the foundation for more and more marketing decisions, digital marketers have been tasked with sorting through an unprecedented amount of data.

This process usually involves hours of digging through analytics, collecting data points from marketing campaigns that span several months. And while focusing on data analysis and post-mortems is incredibly valuable, doing so takes a significant amount of time and resources away from future marketing initiatives.

As advancements in technology scale exponentially, the divide between teams that do and those that don’t will become more apparent. Those that don’t evolve will stumble and those that embrace data will grow — this is where machine learning can help.

Marketers that don’t embrace data will fumble. Those that do will grow — ML can help. Click To Tweet

That being said, machine learning isn’t something digital marketers can implement themselves after reading a quick tutorial. It’s more comparable to having a Ferrari in your driveway when you don’t know how to drive standard… or maybe you can’t even drive at all.

Until the day when implementing a machine learning system is just a YouTube video away, digital marketers could benefit from keeping a close eye on the companies that are incorporating machine learning into their products, and assessing whether they can help with their department’s pain points.

So how are marketers currently implementing machine learning to make decisions based on data rather than gut instinct? There are many niches in marketing that are becoming more automated. Here are a few that stand out.

Lead scoring and machine learning

Lead scoring is a system that allows marketers to gauge whether a prospect is a qualified lead and thus worth pursuing. Once marketing and sales teams agree on the definition of a “qualified lead,” they can begin assigning values to different qualified lead indicators, such as job title, company size and even interaction with specific content.

These indicators paint a more holistic picture of a lead’s level of interest, beyond just a form submission typically associated with lead generation content like ebooks. And automating lead scoring takes the pressure off marketers having to qualify prospects via long forms, freeing them up to work on other marketing initiatives.

Once the leads have reached the “qualified” threshold, sales associates can then focus their efforts on those prospects — ultimately spending their time and money where it matters most.

Content marketing and copywriting

Machine learning models can analyze data points beyond just numbers — including words on your website, landing page or PPC ads. Machine learning systems can find patterns in language and detect words that elicit the most clicks or engagement.

Is emotional copywriting on your landing page effective in your industry?

We used machine learning to help create the Unbounce Conversion Benchmark Report, which shares insights on how different aspects of page copy correspond to conversion rates across 10 industries.

By entering your email you'll receive other resources to help you improve your conversion rates.

But can a machine write persuasive copy? Maybe, actually.

A New York-based startup called Persado offers a “cognitive content platform” that uses math, data, natural language processing, emotional language data and machine learning systems to serve the best copy and images to spur prospects into action. It does this by analyzing all the language data each client has ever interacted with and serving future prospects with the best possible words or phrases. An A/B test could never achieve this at the same scale.

Think this is a joke? With over $65 million in venture capital and a reported average conversion rate uplift of 49.5% across 4,000 campaigns, Persado’s business model is no laughing matter.

Still, there is no replacement for a supremely personalized piece of content delivered straight to your client’s inbox — an honest call to action from one human to another.

Recently Unbounce’s Director of Campaign Strategy, Corey Dilley, sent an email to our customers. It had no sales pitch, no call to action button. It was just Corey reaching out and saying, “Hey.”

Corey’s email had an open rate of 41.42%, and he received around 80 personal responses. Not bad for an email written by a human!

Sometimes it’s actions — like clicks and conversions — you want to elicit from customers. Other times the goal is to build rapport. In some cases we should let the machines do the work, but it’s up to the humans to keep the content, well, human.

There is no replacement for personalized content and an honest ask from one human to another. Click To Tweet

Machine learning for churn prediction

In the SaaS industry, churn is a measure of the percentage of customers who cancel their recurring revenue subscriptions. According to Tommy, churn tells a story about “how your customers behave and feel. It’s giving a voice to the customers that we don’t have time or the ability to talk to.”

Self-reporting methods such as polls and surveys are another good way to give a voice to these customers. But they’re not always scalable — large data sets can be hard for humans to analyze and derive meaning from.

Self-reporting methods can also skew your results. Tommy explains:

The problem with things like surveys and popups is that they’re only going to tell you what you’ve asked about, and the type of people that answer surveys are already a biased set.

Machine learning systems, on the other hand, can digest a larger number of data points, and with far less bias. Ideally the data is going to reveal what marketing efforts are working, thus leading to reduced churn and helping to move customers down the funnel.

This is highly relevant for SaaS companies, whose customers often sign up for trials before purchasing the product. Once someone starts a trial, the marketing department will start sending them content in order to nurture them into adopting the service and become engaged.

Churn models can help a marketing team determine which pieces of content lead to negative or positive encounters — information that can inform and guide the optimization process.

Ethical Implications of Machine Learning in Marketing

We hinted at the ethical implications of machine learning in marketing, but it deserves its own discussion (heck, it deserves its own book). The truth is, machine learning systems have the potential to cause legitimate harm.

According to Carl Schmidt, Co-Founder and Chief Technology Officer at Unbounce:

Where we are really going to run into ethical issues is with extreme personalization. We’re going to teach machines how to be the ultimate salespeople, and they’re not going to care about whether you have a compulsive personality… They’re just going to care about success.

This could mean targeting someone in rehab with alcohol ads, or someone with a gambling problem with a trip to Las Vegas. The machine learning system will make the correlation, based on the person’s internet activity, and it’s going to exploit that.

Another dilemma we run into is with marketing aimed at affecting people’s emotions. Sure copywriters often tap into emotions in order to get a desired response, but there’s a fine line between making people feel things and emotional manipulation, as Facebook discovered in an infamous experiment.

If you aren’t familiar with the experiment, here’s the abridged version: Facebook researchers adapted word count software to manipulate the News Feeds of 689,003 users to determine whether their emotional state could be altered if they saw fewer positive posts or fewer negative posts in their feeds.

Posts were deemed either positive or negative if they contained at least one positive or negative word. Because researchers never saw the status updates (the machine learning system did the filtering) technically it fell within Facebook’s Data Use Policy.

However, public reaction to the Facebook experiment was generally pretty scathing. While some came to the defense of Facebook, many criticized the company for breaching ethical guidelines for informed consent.

In the end, Facebook admitted they could have done better. And one good thing did come out of the experiment: It now serves as a benchmark for when machine learning goes too far, and as a reminder for marketers to continually gut-check themselves.

For Carl, it comes down to intent:

If I’m Facebook, I might be worried that if we don’t do anything about the pacing and style of content, and we’re inadvertently presenting content that could be reacted to negatively, especially to vulnerable people, then we would want to actively understand that mechanism and do something about it.

While we may not yet have a concrete code of conduct around machine learning, moving forward with good intentions and a commitment to do no harm is a good place to start.

The Human Side of Machine Learning

Ethical issues aside, the rise of machines often implies the fall of humans. But it doesn’t have to be one or the other.

“You want machines to do the mundane stuff and the humans to do the creative stuff,” Carl says. He continues:

Computers are still not creative. They can’t think on their own, and they generally can’t delight you very much. We are going to get to a point where you could probably generate highly personal onboarding content by a machine. But it [will have] no soul.

That’s where the human aspect comes in. With creativity and wordsmithing. With live customer support. Heck, it takes some pretty creative data people to come up with an algorithm that recognizes faces with 98% accuracy.

Imagine a world where rather than getting 15 spam emails a day, you get just one with exactly the content you would otherwise be searching for — content written by a human, but served to you by a machine learning system.

While pop culture may say otherwise, the future of marketing isn’t about humans (or rather, marketers) versus machines. It’s about marketers using machines to get amazing results — for their customers and their company.

Machine learning systems may have an edge when it comes to data sorting, but they’re missing many of the things that make exceptional marketing experiences: empathy, compassion and a true understanding of the human experience.

Editor’s note: This article originally appeared in The Split, a digital magazine by Unbounce.

from RSSMix.com Mix ID 8217493 http://unbounce.com/online-marketing/marketing-machines-is-machine-learning-helping-marketers-or-making-us-obsolete/

[racheltgibsau]

Marketing Machines: Is Machine Learning Helping Marketers or Making Us Obsolete?

Hollywood paints a grim picture of a future populated by intelligent machines. Terminator, 2001: A Space Odyssey, The Matrix and countless other films show us that machines are angry, they’re evil and — if given the opportunity — they will not hesitate to overthrow the human race.

Films like these serve as cautionary tales about what could happen if machines gain consciousness (or some semblance of). But in order for that to happen humans need to teach machines to think for themselves. This may sound like science fiction but it’s an actual discipline known as machine learning.

The machines are coming. But fear not — they could help you become a better marketer. Image via Shutterstock.

Still in its infancy, machine learning systems are being applied to everything from filtering spam emails, to suggesting the next series to binge-watch and even matching up folks looking for love.

For digital marketers, machine learning may be especially helpful in getting products or services in front of the right prospects, rather than blanket-marketing to everyone and adding to the constant noise that is modern advertising. Machine learning will also be key to predicting customer churn and attribution: two thorns in many digital marketers’ sides.

Despite machine learning’s positive impact on the digital marketing field, there are questions about job security and ethics that cannot be swept under the rug. Will marketing become so automated that professional marketers become obsolete? Is there potential for machine learning systems to do harm, whether by targeting vulnerable prospects or manipulating people’s emotions?

These aren’t just rhetorical questions. They get to the heart of what the future of marketing will look like — and what role marketers will play in it.

What is Machine Learning?

Machine learning is a complicated subject, involving advanced math, code and overwhelming amounts of data. Luckily, Tommy Levi, Director of Data Science at Unbounce, has a PhD in Theoretical Physics. He distills machine learning down to its simplest definition:

You can think of machine learning as using a computer or mathematics to make predictions or see patterns in data. At the end of the day, you’re really just trying to either predict something or see patterns, and then you’re just using the fact that a computer is really fast at calculating.

You may not know it, but you likely interact with machine learning systems on a daily basis. Have you ever been sucked into a Netflix wormhole prompted by recommended titles? Or used Facebook’s facial recognition tool when uploading and tagging an image? These are both examples of machine learning in action. They use the data you input (by rating shows, tagging friends, etc.) to produce better and more accurate suggestions over time.

Other examples of machine learning include spell check, spam filtering… even internet dating — yes, machine learning has made its way into the love lives of many, matching up singles using complicated algorithms that take into consideration personality traits and interests.

Machine learning may be helpful in getting products or services in front of the right prospects. Click To Tweet

How Machine Learning Works

While it may seem like witchcraft to the layperson, running in the background of every machine learning system we encounter is a human-built machine that would have gone through countless iterations to develop.

Facebook’s facial recognition tool, which can recognize your face with 98% accuracy, took several years of research and development to produce what is regarded as cutting-edge machine learning.

So how exactly does machine learning work? Spoiler alert: it’s complicated. So without going into too much detail, here’s an introduction to machine learning, starting with the two basic techniques.

Supervised learning

Supervised learning systems rely upon humans to label the incoming data — at least to begin with — in order for the systems to better predict how to classify future input data.

Gmail’s spam filter is a great example of this. When you label incoming mail as either spam or not spam, you’re not only cleaning up your inbox, you’re also training Gmail’s filter (a machine learning system) to identify what you consider to be spam (or not spam) in the future.

Unsupervised learning

Unsupervised learning systems use unlabeled incoming data, which is then organized into clusters based on similarities and differences in the data. Whereas supervised learning relies upon environmental feedback, unsupervised learning has no environmental feedback. Instead, data scientists will often use a reward/punishment system to indicate success or failure.

According to Tommy, this type of machine learning can be likened to the relationship between a parent and a young child. When a child does something positive they’re rewarded. Likewise, when “[a machine] gets it right — like it makes a good prediction — you kind of give it a little pat on the back and you say good job.”

Like any child (or person for that matter), the system ends up trying to maximize the positive reinforcement, thus getting better and better at predicting.

The Power of Machine Learning

A lot of what machine learning can do is yet to be explored, but the main benefit is its ability to wade through and sort data far more quickly and efficiently than any human could, no matter how clever.

Tommy is currently experimenting with an unsupervised learning system that clusters landing pages with similar features. Whereas one person could go through a few hundred pages in a day, this model can run through 300,000 pages in 20 minutes.

How do your landing page conversion rates compare against your industry competitors?

We analyzed the behavior of 74,551,421 visitors to 64,284 lead generation landing pages. Now we want to share average industry conversion rates with you in the Unbounce Conversion Benchmark Report.

By entering your email you'll receive other resources to help you improve your conversion rates.

The advantage is not just speed, it’s also retention and pattern recognition. Tommy explains:

To go through that many pages and see those patterns and hold it all in memory and be able to balance that — that’s where the power is.

For some marketers, this raises a troubling question: If machine learning systems solve problems by finding patterns that we can’t see, does this mean that marketers should be worried about job security?

The answer is more nuanced than a simple yes or no.

Machine Learning and the Digital Marketer

As data becomes the foundation for more and more marketing decisions, digital marketers have been tasked with sorting through an unprecedented amount of data.

This process usually involves hours of digging through analytics, collecting data points from marketing campaigns that span several months. And while focusing on data analysis and post-mortems is incredibly valuable, doing so takes a significant amount of time and resources away from future marketing initiatives.

As advancements in technology scale exponentially, the divide between teams that do and those that don’t will become more apparent. Those that don’t evolve will stumble and those that embrace data will grow — this is where machine learning can help.

Marketers that don’t embrace data will fumble. Those that do will grow — ML can help. Click To Tweet

That being said, machine learning isn’t something digital marketers can implement themselves after reading a quick tutorial. It’s more comparable to having a Ferrari in your driveway when you don’t know how to drive standard… or maybe you can’t even drive at all.

Until the day when implementing a machine learning system is just a YouTube video away, digital marketers could benefit from keeping a close eye on the companies that are incorporating machine learning into their products, and assessing whether they can help with their department’s pain points.

So how are marketers currently implementing machine learning to make decisions based on data rather than gut instinct? There are many niches in marketing that are becoming more automated. Here are a few that stand out.

Lead scoring and machine learning

Lead scoring is a system that allows marketers to gauge whether a prospect is a qualified lead and thus worth pursuing. Once marketing and sales teams agree on the definition of a “qualified lead,” they can begin assigning values to different qualified lead indicators, such as job title, company size and even interaction with specific content.

These indicators paint a more holistic picture of a lead’s level of interest, beyond just a form submission typically associated with lead generation content like ebooks. And automating lead scoring takes the pressure off marketers having to qualify prospects via long forms, freeing them up to work on other marketing initiatives.

Once the leads have reached the “qualified” threshold, sales associates can then focus their efforts on those prospects — ultimately spending their time and money where it matters most.

Content marketing and copywriting

Machine learning models can analyze data points beyond just numbers — including words on your website, landing page or PPC ads. Machine learning systems can find patterns in language and detect words that elicit the most clicks or engagement.

Is emotional copywriting on your landing page effective in your industry?

We used machine learning to help create the Unbounce Conversion Benchmark Report, which shares insights on how different aspects of page copy correspond to conversion rates across 10 industries.

By entering your email you'll receive other resources to help you improve your conversion rates.

But can a machine write persuasive copy? Maybe, actually.

A New York-based startup called Persado offers a “cognitive content platform” that uses math, data, natural language processing, emotional language data and machine learning systems to serve the best copy and images to spur prospects into action. It does this by analyzing all the language data each client has ever interacted with and serving future prospects with the best possible words or phrases. An A/B test could never achieve this at the same scale.

Think this is a joke? With over $65 million in venture capital and a reported average conversion rate uplift of 49.5% across 4,000 campaigns, Persado’s business model is no laughing matter.

Still, there is no replacement for a supremely personalized piece of content delivered straight to your client’s inbox — an honest call to action from one human to another.

Recently Unbounce’s Director of Campaign Strategy, Corey Dilley, sent an email to our customers. It had no sales pitch, no call to action button. It was just Corey reaching out and saying, “Hey.”

Corey’s email had an open rate of 41.42%, and he received around 80 personal responses. Not bad for an email written by a human!

Sometimes it’s actions — like clicks and conversions — you want to elicit from customers. Other times the goal is to build rapport. In some cases we should let the machines do the work, but it’s up to the humans to keep the content, well, human.

There is no replacement for personalized content and an honest ask from one human to another. Click To Tweet

Machine learning for churn prediction

In the SaaS industry, churn is a measure of the percentage of customers who cancel their recurring revenue subscriptions. According to Tommy, churn tells a story about “how your customers behave and feel. It’s giving a voice to the customers that we don’t have time or the ability to talk to.”

Self-reporting methods such as polls and surveys are another good way to give a voice to these customers. But they’re not always scalable — large data sets can be hard for humans to analyze and derive meaning from.

Self-reporting methods can also skew your results. Tommy explains:

The problem with things like surveys and popups is that they’re only going to tell you what you’ve asked about, and the type of people that answer surveys are already a biased set.

Machine learning systems, on the other hand, can digest a larger number of data points, and with far less bias. Ideally the data is going to reveal what marketing efforts are working, thus leading to reduced churn and helping to move customers down the funnel.

This is highly relevant for SaaS companies, whose customers often sign up for trials before purchasing the product. Once someone starts a trial, the marketing department will start sending them content in order to nurture them into adopting the service and become engaged.

Churn models can help a marketing team determine which pieces of content lead to negative or positive encounters — information that can inform and guide the optimization process.

Ethical Implications of Machine Learning in Marketing

We hinted at the ethical implications of machine learning in marketing, but it deserves its own discussion (heck, it deserves its own book). The truth is, machine learning systems have the potential to cause legitimate harm.

According to Carl Schmidt, Co-Founder and Chief Technology Officer at Unbounce:

Where we are really going to run into ethical issues is with extreme personalization. We’re going to teach machines how to be the ultimate salespeople, and they’re not going to care about whether you have a compulsive personality… They’re just going to care about success.

This could mean targeting someone in rehab with alcohol ads, or someone with a gambling problem with a trip to Las Vegas. The machine learning system will make the correlation, based on the person’s internet activity, and it’s going to exploit that.

Another dilemma we run into is with marketing aimed at affecting people’s emotions. Sure copywriters often tap into emotions in order to get a desired response, but there’s a fine line between making people feel things and emotional manipulation, as Facebook discovered in an infamous experiment.

If you aren’t familiar with the experiment, here’s the abridged version: Facebook researchers adapted word count software to manipulate the News Feeds of 689,003 users to determine whether their emotional state could be altered if they saw fewer positive posts or fewer negative posts in their feeds.

Posts were deemed either positive or negative if they contained at least one positive or negative word. Because researchers never saw the status updates (the machine learning system did the filtering) technically it fell within Facebook’s Data Use Policy.

However, public reaction to the Facebook experiment was generally pretty scathing. While some came to the defense of Facebook, many criticized the company for breaching ethical guidelines for informed consent.

In the end, Facebook admitted they could have done better. And one good thing did come out of the experiment: It now serves as a benchmark for when machine learning goes too far, and as a reminder for marketers to continually gut-check themselves.

For Carl, it comes down to intent:

If I’m Facebook, I might be worried that if we don’t do anything about the pacing and style of content, and we’re inadvertently presenting content that could be reacted to negatively, especially to vulnerable people, then we would want to actively understand that mechanism and do something about it.

While we may not yet have a concrete code of conduct around machine learning, moving forward with good intentions and a commitment to do no harm is a good place to start.

The Human Side of Machine Learning

Ethical issues aside, the rise of machines often implies the fall of humans. But it doesn’t have to be one or the other.

“You want machines to do the mundane stuff and the humans to do the creative stuff,” Carl says. He continues:

Computers are still not creative. They can’t think on their own, and they generally can’t delight you very much. We are going to get to a point where you could probably generate highly personal onboarding content by a machine. But it [will have] no soul.

That’s where the human aspect comes in. With creativity and wordsmithing. With live customer support. Heck, it takes some pretty creative data people to come up with an algorithm that recognizes faces with 98% accuracy.

Imagine a world where rather than getting 15 spam emails a day, you get just one with exactly the content you would otherwise be searching for — content written by a human, but served to you by a machine learning system.

While pop culture may say otherwise, the future of marketing isn’t about humans (or rather, marketers) versus machines. It’s about marketers using machines to get amazing results — for their customers and their company.

Machine learning systems may have an edge when it comes to data sorting, but they’re missing many of the things that make exceptional marketing experiences: empathy, compassion and a true understanding of the human experience.

Editor’s note: This article originally appeared in The Split, a digital magazine by Unbounce.

from RSSMix.com Mix ID 8217493 http://unbounce.com/online-marketing/marketing-machines-is-machine-learning-helping-marketers-or-making-us-obsolete/

[olgagarmash]

The video posted to TikTok showed a woman in a blue cardigan and brown medical scrubs dancing to a remix of Wale’s “Lotus Flower Bomb.”

On screen, sandwiched between two sparkle emojis, the woman, who said she was a pharmacy technician, had written, “Most common meds I’ve filled that cause cancer.” She then went on to claim medications like hormonal birth control, cholesterol medications and chemotherapy were cancer causing.

So, Savannah Sparks, another TikTok user who goes by “Rx0rcist,” made her own video, part of what would become an ongoing series debunking medical misinformation on the app.

“My name’s Savannah. I’m a doctor at a pharmacy, and I’m about to absolutely wreck your s—,” Sparks says in the video before launching into a fact-check of the pharmacy technician’s claims.

But Sparks didn’t stop there. She then contacted the woman’s supervisor.

“Her scope of practice doesn’t allow her … to counsel on medications so, especially coming from the realm of pharmacy, which is my wheelhouse, I really went in on that individual and I was like, ‘You really should not be talking about this,'” Sparks said.

Sparks, 31, a Mississippi-based lactation consultant and doctor of pharmacy who is also a mother of a 2-year-old daughter, has become a prolific watchdog on TikTok for those she says are trying to spread misinformation — especially health care workers spreading bogus information about Covid-19.

“In the past, I have been a little more reserved with how aggressive I have gone after these people, but the longer this pandemic went on, and the more and more misinformation we started seeing as health care workers on social media, the less I started caring about my tone and coming across a certain way,” Sparks said.

This has earned her a massive following on TikTok. Her account has more than 467,000 followers and her videos rack in hundreds of thousands — and sometimes millions — of views.

Sparks said she is not only looking for the removal of health care misinformation on the platform, but she also wants accountability.

“Anything that forces somebody to change their way of thinking … it makes them angry,” Sparks said. “So, keeping that in mind, the fact that I’m doing this to so many people, I accept I’m doing exactly what I need to be doing, and I’m exactly where I need to be.”

This approach to calling alleged offenders out has made her the target of online harassment. Her address has been posted on extremist websites, and her inboxes have been flooded with threats of rape and death against both her and her daughter, which, at one point, became so relentless it nearly drove her off the internet.

Misinformation and callouts

Sparks’ most exhaustive callouts are part of a series on her TikTok that she calls “Petty Journal Club with Sav.” She said the videos began as a way to thwart general health care misinformation from spreading on the app, but soon morphed to be more specific as she said she realized some health care workers were not only propagating misinformation about the pandemic, but also teaching their followers how they could get around Covid restrictions.

Using public information and social media, Sparks said she would identify the TikTokers making dubious claims or bragging about skirting rules and contact their employers or, in the most egregious cases, their respective field’s licensing board in an attempt to hold them accountable.

And with TikTok’s algorithm frequently elevating Sparks’ videos to the “For You” page, the platform’s infinite scroll homepage, she continued to draw in even more viewers and followers.

Sparks decides how to handle bad actors on a case-by-case basis, she said, contacting a person privately if she feels their intent is not malicious. If a person makes what she thinks is a major misstep — like a health care worker saying they don’t wear masks outside of work, spreading misinformation about medications or stealing vaccination cards — Sparks said she will share that person’s offending TikTok with her followers, explaining why the person is wrong.

“It’s different for each case depending on how much information I can get on an individual and how egregious their error was online, because some aren’t as bad as others,” Sparks said.

Sparks says one of her first “Petty Journal Club with Sav” videos was the pharmacy technician, who claimed certain medications cause cancer.

When Sparks contacted the woman’s supervisor on Facebook, the supervisor was shocked, she said.

“She was like, ‘Oh, my gosh. I’m ashamed. I can’t believe she’s posting that kind of information,’” Sparks recalled.

Karen North, a professor of digital social media at the University of Southern California’s Annenberg School for Communication and Journalism, said one reason viewers are drawn to this type of content is because it’s like a catharsis for their real-life frustration around rule breakers.

“We all know people who have done things that step over the lines in terms of what we think is right during a pandemic, whether it’s not wearing a mask or being anti-vaxxers or jumping the line to get a vaccine … to the extent we’re frustrated by people we know in our own social circles who are breaking our rules. We can now go online and not only watch someone break a rule but watch someone attack someone for breaking a rule,” North said.

After a public callout on her page, Sparks said, the subject will sometimes go private or delete their various social media accounts.

Sparks says she is meticulous about her work and knows she has a responsibility to do her due diligence first because her callouts could have hundreds of thousands of eyes on them and serious ramifications for the poster.

“Even if they volunteer all that information on their own, linking their social media and where they work, unless I can be pretty certain that what they’re saying is not a joke or what they’re saying does have some malicious intent, I’m not going to push hard because I know that when I go in, I go all in,” she said.

She does, however, recall once getting a detail of a callout wrong. A nurse, whom she had called out, listed a hospital as an employer on her Facebook, which Sparks included in a video about the nurse. The only problem? The nurse no longer worked there and a horde of Sparks’ followers had contacted the facility demanding that person be fired.

“People started calling that hospital and then I reached out to the hospital directly and said, ‘This is what has happened. I’m sorry,’” Sparks said.

The roots of callout culture

Jessa Lingel, an associate professor at the Annenberg School for Communication at the University of Pennsylvania who studies digital culture, said callout culture has a long history on social media, and began as a way for people of color to create accountability around major social issues.

“Cancel culture, callout culture, that really comes from practices on Black Twitter of bringing attention to an issue and saying, hey, this is a thing where we need to align. Whether it’s #MeToo in its early days, that originated on Black Twitter, or whether that’s tied to Black Lives Matter or police brutality. Callout culture originated on Black Twitter,” she said.

Lingel added that callout culture has since evolved from a political tool into a way individuals can get back at one another on social media for real or perceived grievances. This often gives way to someone being labeled a “Karen.”

But Sparks has embraced the Karen moniker when it comes to her TikTok content — and she’s not the only one.

TikToker Aunt Karen, 31, who asked that NBC News not use her real name or location in order to protect her safety, is renowned on the app for calling out those who have been caught engaging in racist behaviors.

“The internet has always been a tool, but now it’s an even bigger tool and it’s the main frame for holding people accountable,” Aunt Karen said.

Behind the scenes, Sparks and Aunt Karen said the people who make content calling out bad behavior on the internet, many of whom are women, have built a network supporting one another, and sometimes work together.

“What I think is great is even though we all call people out, there’s different things that these creators speak out on. Aunt Karen talks a lot about racism and, as [she’s] a woman of color, I can learn a lot from that … Not only do I get to make a friend but I learn a ton from these people,” Sparks said.

While experts say Sparks and Aunt Karen’s callouts — which have collectively drawn millions of views — can provide a counternarrative to those seeking more information, they’re doubtful TikTok vigilantism will change people’s deep-seated views, adding that research into online shaming shows it doesn’t generally bring about significant change.

Related

“Health care workers during Covid have enjoyed a lot of public support generally speaking and so that doesn’t mean mistakes can’t be made and that we shouldn’t pay attention to those mistakes. But, in general, the research on online shaming is not optimistic on whether any of this is going to have much of an impact,” Lingel said.

Research also shows that online shaming is inherently impossible to police and can devolve into abuse, including threats of physical or sexual violence. Moreover, online shaming tends to dehumanize those on the receiving end and can turn a person who has violated a social norm into a target undeserving of empathy in the eyes of an online mob.

Harassment

The subjects of callout culture are not the only ones who have had to pay a price for having the eyes of the internet locked on them.

On March 28, Sparks posted a video announcing she was stepping away from TikTok because of an onslaught of harassment.

She said her address and phone number were posted online, and that her direct messages on Instagram were flooded with death threats directed both at her and her young daughter. Her business pages were bombed with negative reviews. And links to her TikTok account were posted to extremist forum 4chan.

“They posted aerial photos of my mom’s house on 4chan, which they paired next to a video of me and my sister dancing in her backyard to confirm that I was still at her house so they could plan to murder, rape, and kill me,” Sparks said.

Sparks said she had always endured modest backlash for her content, but the harassment ratcheted up in March to the point it became unbearable.

“I was getting probably a hundred [direct messages] a day, just every few minutes in my message requests on Instagram, in comments,” she said, recalling that she was sent messages “saying things like, ‘Kill yourself,’ ‘I’m going to rape you,’ ‘I’m going to rape your daughter,’ Very graphic.”

The wave of ceaseless harassment and threats began, she said, after she posted a video about safety precautions she takes when running and got worse when she began calling out the alleged forged vaccine cards that some health care workers were bragging about on TikTok.

“They went to my Facebook business page, they found my family, they found all my friends and started messaging them. Same thing, just graphic kinds of death threats,” Sparks said.

Then, she said, when her information ended up on 4chan, she said trolls began contacting businesses she affiliates with as a lactation consultant, claiming she was a racist and asking that they no longer do business with her. The attacks continued to escalate until someone posted her phone number and the aerial photo of her mother’s house.

NBC News reviewed nearly 20 of the threats sent to Sparks, some of which were sent by accounts with names like “times_up_savannah,” created solely to harass her.

Related

Sparks eventually filed a complaint with her local sheriff’s office and then made the decision to make her callout videos private and step away from TikTok.

But about two weeks later she returned to the app. She said she feels it’s her “duty to stand up and do the right thing,” emphasizing that she wants to use her platform to be an ally to marginalized voices and to others like Aunt Karen, who are also making callout content on TikTok.

“If I’m not willing to do it, who else would step up to do it?” Sparks said. “… A lot of people say, ‘Well, it’s not a big deal, it’s just TikTok.’ But the things that I talk about are a huge deal. Public health is a huge deal, especially when 500,000 Americans have died from this virus.”

via Wealth Health