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tastydregs · 4 years ago

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The global business of professional trolling

Professional political trolling is still a thriving underground industry around the world, despite crackdowns from the biggest tech firms.

Why it matters: Coordinated online disinformation efforts offer governments and political actors a fast, cheap way to get under rivals' skin. They also offer a paycheck to people who are eager for work, typically in developing countries.

"It's a more sophisticated means of disinformation to weaken your advisories," said Todd Carroll, CISO and VP of Cyber Operations at CybelAngel.

Driving the news: Facebook last week said it had uncovered a massive troll farm in Albania, linked to an Iranian militant group.

The operation had the the hallmarks of a typical troll farm, which Facebook defines as "a physical location where a collective of operators share computers and phones to jointly manage a pool of fake accounts as part of an influence operation."

"The main thing we saw was strange signals centralized coordination between different fake accounts," said Ben Nimmo, Facebook's global influence operations threat intelligence lead.

Like numerous troll farms uncovered over the past few years, there was one easy giveaway: content from the network targeted Iran, but was posted on social media during normal working hours on Central European Time.

Be smart: The best way to slow down professional trolls is to make it more expensive for them to carry out disinformation campaigns, says Jean le Roux, a researcher at the Atlantic Council's Digital Forensic Research Lab (DFRLab).

He, along with his team at the DFRLab and Buzzfeed News, uncovered a large troll operation in Nigeria last week, in which a Nigerian PR firm and a UK-based nonprofit paid social media influencers in Nigeria to tweet support for a Columbian businessman accused of money laundering in the US.

The operation aimed to recruit influencers, primarily in Nigeria, to tweet in support of the businessman, Alex Saab, twice per week initially for one month earlier this year, according to documents obtained by BuzzFeed News.

Twitter last week suspended more than 1,500 accounts for manipulating the #FreeAlexSaab hashtag, per the report, but told Buzzfeed News it hasn't yet determined whether this was a state-backed campaign.

The big picture: Professional troll farms tend to share key attributes, which help to tip tech platforms and researchers off.

Shared physical location: Troll farms are often propped up by a party that will pay for high-speed internet and computers that together power the network. It's easier to finance and monitor operations that physically sit close together.

Posting during working hours: As was the case in the Albanian operation, content from troll farms tend to be posted during work hours, with breaks for lunch and during the evening.

Hyper-targeted messaging: Posts from troll farms tend to zero in on a certain political message. Most ordinary people post about an array of topics, le Roux notes.

What to watch: Troll farms can create a symbiotic relationship between political actors eager to manipulate adversaries and developing nations eager for cash.

CNN, in conjunction with Clemson University, last year uncovered a major troll operation in Ghana being used to sow division among Americans ahead of the 2020 election. The operation was linked back to the Russian state-backed troll operation called the Internet Research Agency.

Carroll, a 20-year veteran with the FBI, said that in his time investing troll operations, he saw many from places like Vietnam, Philippines, and Malaysia — "places where there's a lot of cheap labor and little oversight."

"We're also seeing a lot more (troll operations) being picked up in Africa," le Roux says, which is what has in-part inspired the DFRLab to open an office in the region, where le Roux is based.

"More people in Africa are going online, on social," he says. "At same time, Africa is one of poorer continents, which creates an easy recipe for someone like Russia to step in and pay someone to sit behind a computer all day."

Often, bad actors will go to lengths to set up "cut-outs" or systems to pay trollers without having to go through a bank, or a system that would get them noticed. Usually, money is distributed via a third-party on the ground.

Bottom line: Trolling requires few technical skills, Carroll notes, and it pays. "Any emerging economies susceptible something like that," le Roux says.

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netxinformationsystemsinc · 5 years ago

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https://lnkd.in/eMQfXEd Register Today hashtag#veritastraining hashtag#veritas hashtag#symantectraining hashtag#symantec hashtag#cybersecurity hashtag#cybersecuritytraining hashtag#dataprotection hashtag#ciso hashtag#endpointsecurity hashtag#endpointprotection hashtag#endpointmanagement

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americanfreighttrucking · 6 years ago

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Veriphyr Advisor Steve Katz Receives Lifetime Achievement Award for Contributions to Information Security Industry’s First CISO Recognized by Shared Risk Assessments Program

LOS ALTOS, Calif.–(BUSINESS WIRE)–lt;a href="https://twitter.com/hashtag/HIPPA?src=hash" target="_blank"gt;#HIPPAlt;/agt;–Veriphyr, a leader in healthcare patient data privacy monitoring, announced today that strategic advisor Steve Katz, executive advisor on privacy and security for Deloitte and advisor to the Board of the H-ISAC (Health Information Sharing and Analysis Center), has…

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#Trucking News

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patrickcmiller · 6 years ago

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via Twitter https://twitter.com/PatrickCMiller

#IFTTT #Twitter

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nrip · 6 years ago

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Healthcare tweet Extraction, Visualisation and Particle Swarm Optimisation using Python

We all live in a world where analyzing a massive set of unstructured data is becoming a business need. And the time we spend on the internet is basically the time we spend on social media. Even our daily life is affected by the people around us. And we are tending to change our opinions and thoughts towards something based on other people’s ideas and opinions. Some may call this bad and at the same time, some may call it good. Anyway, if we have the ability to think of other’s opinion that’d be really helpful and that’s when sentiment analyzes comes up. sentiment analysis we do classification of the data samples into positive, negative or neutral classes and bring out proper conclusions from the data.

we have done a meta-analysis of data obtained from 20 peer-reviewed scientific research studies (2009–2017) describing the Sentimental analysis using PSO and how PSO can help increase the productivity of twitter.

Key Words — sentiment, twitter, PSO, cluster, big data

Introduction

Swarm is a large number of agents interacting locally with themselves. In Swarm there’s no supervisor or central control to give orders of how to behave. Swarm-based algorithms are popular in this age hence its’s passion of nature-inspired, population-based algorithms that can produce high quality product with low cost and fast solutions to situations that are identified as complex and hard to solve [1][2]. Because of that reason Swarm intelligence is becoming a million-dollar gem in the category of Artificial Intelligence that is ready to collect the pattern, lifestyle and behavior of social swarms in the environment, for example, bird flocks, honey bees, ant colonies, and fish schooling. Even though these agents (These insects/ Swarm individuals) are simpleminded with not enough experience on their own, they all work together to achieve a common goal for their survival.

Bird Flocks

Ant Colonies

Natural Behaviour of ants

It’s not a matter of knowledge and the main mechanism of this interaction is called stigmergy which uses agents or actions for indirect coordination [3]. According to [3] Stigmergy is a well-defined form of self-organization. It produces a complex structure, seemingly intelligent structures, without the need for any control, planning or any direct communication in between the individual agents.

The PSO algorithms are most fascinating and pulled in to the researcher in the field of the fuzzy logic system, neural network, optimization, pattern recognition, robot technology, signal processing, etc[4].

To delete unnecessary duplicate attributes from a dataset we can use feature selection methods. Removing those attributes is a must because at the end of the day we do not want to decrease the accuracy of the algorithm which we use to predict something in the future [5].

Feature selecting is not an easy task because the size of the search space, Search space is proportionate increases with the features that are included in the data set [5]. The key principle of the feature selection is to improve the quality and performance of the predictor. Feature selection acts as a bridge for the preprocessing and we are defining the feature and selection process just before we step in to the extraction process. Applications of Feature selection includes data classification, image classification, cluster analysis, data analysis, image retrieval, opinion mining, review analysis, etc. Using a method called wrapper method, feature extraction can be done using two stages: The stage one is, extracting all the Twitter data as a dataset and we transform this tweet into a normal text stage. What we do in the next stage is all about adding more features to the feature vector [1]. A class label is assigned to each tweet data in the training data sector and then pass these data to several classifiers for the process of classification and at the end, we get classified tweet data which are either positive, negative or neutral. Wrapper method achieves superior results than filter methods. FS is seen as an optimization problem because obtaining an optimal subset of relevant features from irrelevant and redundant data is very important. Many evolutionary algorithms have been used for optimizing the feature selection, which includes genetic algorithms and swarm algorithms. Some of the swarm-based optimization algorithms [7] for feature selection include Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC) and Ant Colony Optimization (ACO).

Wrapper Method

In order to do the research scientific articles published on the IEEE related to sentiment analysis were collected and reviewed. All these articles used sentiment analysis on their research in many ways. Those previously work helped this research. Obtained knowledge about the technologies and tools they used for the sentiment analysis and how to obtain results within them. Previous publications have discussed on following matters.

[1] In this paper the researchers did several studies to investigate the use of Twitter to analyze ongoing health-related tweets and they outline that using Twitter data as a channel to reach out to the public to deliver more information about infection, diseases are a more efficient way in this age. English and Arabic tweets are collected from 3 different countries such as Dublin, London, and Boston. When searching and extracting for tweets they used more restricted queries to select the tweets among potentially identified healthcare ones that very likely to be relevant to healthcare. Then we used the narrow-identified tweets to train a support vector machine (SVM) with the linear kernel to classify the remaining identified tweets as relevant or irrelevant to healthcare. They’ve built three data sets a positive(SetP) and a negative(SetN) set for the training the models, and the set of potentially relevant tweets(SetT), which would be classified by the SVM classifier.

SVM Classifier — Linear Kernel

Extensive analysis is applied to the identified health-related tweets from each stream. For a fast and robust matching between tweets, we keep only the main content text of tweets by removing all hashtags, name mentions, URLs, punctuations, symbols, emoticons, and retweet symbols. As a result, they extracted the most popular tweet; most shared link or image related to healthcare then applied sentiment analysis to figure out public satisfaction about health (used sentiStrength tool to figure out sentiment), the top frequent words, terms, and hashtags related to healthcare and topics what people are more likely to discuss about (tweet about). The top frequent words, terms, and hashtags (excluding the stop words) are used to draw a tag cloud, where font size used for the different terms indicates their frequencies. The tag-clouds helps to summarize the most popular terms in the tweets, which in turn indicate the most popular topics people are interested in.

In this paper [2], They majorly target Stroke and Telemedicine; Stroke plays a major part when it comes to Death and disability. Thrombolysis and strokes can be easily treated if the patient is treated within 4.5 Hours. In this paper, they have got feedback from neurologists (Altogether 25 person) from Milan, Naples and Rome. Nearly 50% of the neurologists are worked in general infirmaries and the rest is divided in University Hospitals, Research Hospitals (IRCSS Foundations) and regional hospitals. An online questionnaire was held during the summer of 2017 to evaluate the type/quality of different hospitals, type of structure working, amount of thrombolysis-thrombectomy/ year, the ability of a network to estimate neuroimaging or to organize transfers of patients to other hospitals and finally to evaluate the type of technology that uses in telemedicine for patients or clinical needs. By the help of using Statistical Package for the Social Sciences SPSS software descriptive analysis with absolute and percentage frequencies of the qualitative variables was performed. According to the knowledgeable, in most scenarios (approximately more than 80%), there’s already an established network for treatment of Stroke. Inside that same network, hospital care was not identified as standardized and the network for the consultation of neuroimaging amongst hospitals was missing in 33.33% scenarios. Besides that, network consultations (via telephone or any other mean) were shown 62.5% possibility in the same network and also 43.86% of clinical cases were appeard to be evaluated using video/audio teleconference. Exactly 50% of physicians are showing the adoptability of using their own mobile phones with nonprofessional systems and social media (eg: WhatsApp®), Skype®, Facetime®, Telegram®) while only 25% of doctors use professional teleconference systems (Ciso®) and the rest use medical devices.

[3] In this paper the author has judged the likelihood of using several data sources for the prediction of EDvisits related to Asthma, Twitter to extract tweets related to asthma including asthma-related keywords, air quality data gathered from sensors, historical electronic health records indicating asthma-related visits to an emergency room and also search trends from Google within same time period in a same unique geographical area. Clinical collaboration from PCCI suggested several related keywords to use as a rod to catch public streaming tweets using Twitter API. From 11–10–2013 to 31–12–2013, they’ve been able to gather a dataset consist of 464 845 785 general tweets and 1 315 390 asthma-related tweets. On a regular basis, 15000 asthma related tweets are posting from all across the world in a day. Used ‘coordinates’ and ‘location’ fields to identify the geographical location of each tweet. But in most cases, it’s unable to collect the geographical location of the tweet, in such cases, they track down user’s profile location and extracted that location as the geographical location. First tweets are collected the pre-processed tweet with removing unnecessary content. Then engaged with a machine learning classification technique called artificial neural networks (ANN) to extract asthma-related tweets. In the training dataset each tweet contained at least one asthma-related keyword, and the dataset was divided in to three sections.

An artificial neural network is an interconnected group of nodes.

After that, they developed a prediction model to evaluate the frequency of Asthma ED visits using a combination of independent variables from the aforementioned data sources. Each Dataset was different by one another as they gathered from different data sources. The first step is to transform data into more compatible dataset by normalizing dataset using standard normalization technique (z-score), and then for the prediction model, used four distinctive classification methods Naive Bayes, Decision Trees, ANN, and SVM.

normalizing dataset using standard normalization technique (z-score)

To minimize classification errors, the use of adaptive boosting technique and stacking technique has being used.

[4] According to the author, the scalability of good clustering technique is based upon the estimations of comparability. Clustering is an effective device of seeking and in this paper the author points out that using PSO algorithms to clustering twitter data stream is much more efficient than any other data clustering algorithms. Among many methods proposed for feature extraction, the PSO algorithms work more efficiently. These algorithms find the best solutions according to the knowledge obtained from previous iterations. The whole tweet data clustering method is labeled into four phases: a) Tweet data gathering b) preprocessing of gathered tweets c) Feature extraction and d) Data clustering using PSO. With the use of Twitter API, every 1000ms we developed our program to gather streaming tweets.

Extracting real-time tweets with Python and JSON

Then storing each tweet in a certain file according to its category, as the second phase, the data cleansing is done for cluster analysis and then comes the tokenization to divide each word in a tweet. Then as the last phase, PSO is applied to preprocesses tweets for clustering. The first step is to initialize the number of particles. Afterward of the initialization phase, assigning each tweet to its neighboring centroid vector for each particle is next, the health of each particle is generated by studying the average similarity between the cluster centroid and a tweet in the document vector space. In order to minimize the inter-cluster distances, we measured the distance path between the data vectors within the given cluster. To maximize the cluster distance, we have evaluated the distance between the centroids of the cluster. To the overall reports generated, we have undertaken almost 30 simulations and the proposed algorithm has taken 1000 functions to effectively evaluate the results.

[5] In this paper the author is giving the audience an important message which is the Alerts are going to be an essential art of near future remote health monitoring. For such capability, we must evaluate the practicability of leveraging online social media for such Aptitudes. In here, they have developed a medical notification trigger using facebook API to develop a facebook application. And there are four perspectives this system which is: The view of the patient, the doctor, the professional career, family member and finally the viewpoint of the carer network. The suitability of the application was analyzed as well as an initial examination of the reliability of alert delivery. We conclude that online social media platforms could offer suitable platforms for alerts in remote health monitoring.

[6] In this research paper, the authors try to determine the sentiments of the candidates of the Presidential election of USA, Donald Trump, Hillary Clinton, and Bernie Sanders were among the top election candidates. So here, what they did is extracted relevant, matching tweets for each candidate using keywords, hashtags, and mentions. 10000 labeled tweets are extracted for three candidates (Bernie Sanders, Donald Trump, and Hillary Clinton). What this system meant to do is extracting tweets and accomplishes political sentiment analysis on the gathered dataset. The process includes four stages: i. Retrieval stage, ii. Preprocessing stage, iii. Analysis stage, iv. Visualization stage Public tweets are extracted from Hillary, Trump, and Bernie using Twitter API using python, then all the tweets are preprocessed and filtered into meaningful sentences in the English language. Polarity and the subjectivity is calculated by a unique algorithm and the library NLTK used hence they’ve been working with human language using python framework. TextBlob in python was used to processing of textual data.

[7] In this paper the main goal is to reveal the views of the leaders of two big democratic parties in India, data was collected from the public accounts of Twitter. Opinion Lexicon was used to find a total number of positive, neutral and negative tweets. Findings show that analyzing the public views could help political parties transform their strategies. Extracted real time tweets using Twitter API for the comparison of two political parties. They used two sentiment analyzers named SentiWordNet and WordNet were used to determine the positivity or negativity of the relevant tweet. A negation handling and Word sequence disambiguation (WSD were used to add more accuracy to the model. And the confident of polarity calculation is done by using the library textBlob in python then to validate the results, SVM and Naïve Bais has been used. Tweets are extracted using Tweepy API with the help of standard search tool and gathered 100000 tweets based on around 20 hashtags in both English and Urdu languages. Then they determined the sentiment using 3 analyzers. To validate the results we obtained, converted the .csv files to. arff format then recorded the accuracy of each analyzer under each model.

[8] In this paper the authors have studied different aspects of medical sentiment in health-related texts that may relate to the following: Health status, Degree of medical condition that impacts patient life, Consequence of a treatment, Opinion towards a treatment, certainty of a diagnosis. For this work, we have collected a corpus consisting of 7,490 user blog posts from popular medical forum ‘patient.info’ which is split on the basis of two major medical sentiment aspects, namely ‘medical condition’ and ‘treatment’. The corpus is manually annotated with a predefined set of categories. Finally, a Convolutional Neural Network (CNN) based model is developed for medical sentiment classification. SentiWordNet lexicon is used for sentiment calculation for the analysis. They extracted posts fron medical blogs and observed that patient’s thoughts on a a symptom or medical condition is majorly based on his/her opinion. After deep analysis of data, they have observed that majority of the medical sentiment occurs in the vicinity of the term ‘feel’ and its variations. So, they used the word ‘feel’ as target word and observed that these words can provide an effective clue in capturing the sentiment.

[9] In this paper, real time clustering is done by using static and dynamic algorithms using Hadoop network. They’ve used a combination of PSO and K-Means algorithms which they called as KSPSO. As usual Twitter’s Rest API is used to collect tweets and then the pre-processing with tokenization is being made. Porter Stemmer Algorithm is used to remove stop words in the data set. Document matrix is advised to be prepared after data preprocessing. By testing with various particles, they applied PSO algorithm and discussed about Fitness, Precision and Recall in a cluster.

[10] In this paper; the authors describe about Swarm in detail and how we can use Swarm methodologies such as Particle Swarm Optimization to do a proper feature selection in a cluster analysis. Rapid PSO is identified as an advanced version of PSO. Used various number of python libraries such as NumPy, SciPy, Pandas, Matplotlib for data analysis process.

Using Rapid Particle Swarm Optimization for feature selection have shown incredibly accurate results with measurable levels of Precision and Recall. And also, less amount of time than standard PSO.

The rest of this chapter is structured in the following order. Section II is a description of materials and methods. Section III explains the results of the research. We discuss our findings with relevant literature and summarize our comments and some of the implications of our findings in Sections IV and V.

Material and Methods

Based on Swarm Intelligence a simple mathematical model was developed by Kennedy and Eberhart in 1995 [8], they majorly want to describe and discuss about the social behavior of fish and birds and it was called the Particle Swarm Optimization (PSO). PSO is one of the most famous and very useful metaheuristics in the current age hence it showed the success of various optimization problems after applied on. The basic principle of this model is self-organization that describes the dynamics of complex systems. PSO uses an extremely streamlined model of social conduct to take care of the optimization problems, in a cooperative and intelligent framework.

In twitter, a user is permitted to write their own opinion and views in a short message on the social platform twitter [1] are termed as tweets. Tweets may contain characters, links, media or a recording. These tweets are not written to a standard and it has mostly short structures, slangs, abbreviations, mistakes in grammar, half-written sentences, misspellings and so forth. So, it is perplexing to expel the useful data from tweets because of their unstructured style. By applying to cluster we’re trying to reduce the complexity of finding the occasion or theme of the tweet composed. To extract specific words that are matching to a particular event data is addressed by vector space demonstrate using term recurrence and inverse term recurrence. So, Particle swarm change framework is the best method to handle this issue [5].

The tweet clustering process is consisting of below 4 modules:

1. Collection of Files

2. File pre-processing

3. File clustering with PSO

4. File Post-processing.

1. File Collection

We collect individual tweets related to Telemedicine, Heart Strokes and Epilepsy data and then store each in a separate .csv file. So, at the end of execution of our python code. We were able to collect more than 30000 data for each file based on around 10 keywords for each category (each file):

File 1 — Telemedicine.csv (Telemedicine + Digital Health tweets)

Ø hashtags Used: #telemedicine #telehealth #digitalhealth #ehealth #digitalpatient #digitaltransformation #telemedicinetoday #ehealthissues

File2 — Epilepsy.csv

Ø Hashtags used: Epilepsy #epilepsyawareness #epilepsyaction #epilepsyalerts #epilepsybed #epilepsycongres #epilepsysurgery #epilepsysurgery #Epilepsytreatment #seizures #seizurefree

File3 — HeartStrokes.csv

Ø Hashtags used: #HeartDisease OR #stroke OR #Stroking OR #strokepatient OR #StrokeSurvivor OR #hearthealth OR #Stroke OR #HeartFailure’

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mmatkf · 10 months ago

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Technology plays a pivotal role in every aspect...

Technology plays a pivotal role in every aspect of a business, yet few tech leaders make it to board level. Here’s how CTOs, CIOs and CISOs can take the leap. https://krnfy.bz/3RT0LuK #technology #techleaders #leadership

Technology plays a pivotal role in every aspect...

Technology plays a pivotal role in every aspect of a business, yet few tech leaders make it to board level. Here's how CTOs, CIOs and CISOs can take the leap. https://krnfy.bz/3RT0LuK {hashtag|\#|technology} {hashtag|\#|techleaders} {hashtag|\#|leadership}

Korn Ferry Connect

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unixcommerce · 6 years ago

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Learn of Franchise Business Opportunities at This Silicon Valley Seminar

Franchises offer one of the best entry points into the business world for first-time entrepreneurs. For experienced business people, it has just as many opportunities.

The NorCal Franchise Business Expo 2019, which is sponsored by Silicon Valley Score, will let you meet franchisors face to face.

If you want to be independent, be your own boss, and keep your job while running a business, a franchise might be the answer for you.

Attendees will get an overview for each franchisor and take part in an interactive Q-and-A discussion. They will also have the opportunity to meet with the franchisors, funding specialists, government nonprofit agencies and other resources.

Some of the franchisors who will be at the event include B2B, B2C, retail, health and wellness, education, senior services and more.

There will be two events held in Palo Alto, CA on January 22, 2018, from 2-5 pm and another one in San Ramon, CA on January 23, 2018, from 1-4 pm.

Space is limited so click on the red button to register.

If you attend, you will be entered into a drawing for a chance to win an iPad! The first 50 attendees will receive a free book, E-Myth Revisited.

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