#LinkedIn Data

[etakeh]

If anyone's on the (super uncool but sometimes necessary in order to get a job) website Linkedin, they have made AI data collecting opt-OUT.

So settings and privacy → data privacy → Data for Generative AI Improvement → Off

While you're there, dedicate a good 10 minutes to going through the rest of the settings. THERE ARE SO MANY.

And they're all turned on.

[bloghrexach]

💥 … July 19, 2024!!

Posted by: Abid H, Embracing Humanity, from LinkedIn …

“And this is why we went from 40,000 Palestinians 🇵🇸 #massacred — straight to 186,000 reported by #TheLancet!” —🍉🇵🇸✊🏽 … 💥

@hrexach

[clementine-elegance]

WTF does Linked In need AI for?

[laurelindebear]

So ya girl (and some other staff) are in the local paper as well as having been on BBC radio a month or so ago. It's...interesting. OTOH the Mortifying Ordeal of Being Known and all that, and on the other the satisfaction of actually being recognised for something I'm doing, with my name and everything, and not being an invisible hand, an imperceptible agent of administration and bureaucracy and all the fiddly little paperwork things that nobody but archivists cares about until Things stop working how they ought.

Above it all, the sheer weirdness and incomprehensibility of the idea that anyone would know who I am or give a damn what I think, about anything. Even to myself, I feel like I'm functionally a non-entity, you know? Like I barely exist. I have no idea how many times I've thought, if I disappeared, who would notice? So it's novel and exciting and really a bit terrifying to suddenly have a footprint and a voice, or something.

I think I thought I was gonna be a real Somebody, when I was young and stupid, but it's been a long, long time since then. I can't remember when it stopped; all I know is that younger version of me feels like another person and another life. I'm never going to be the kind of Nobel-Prize-winning legend I aspired to be as a child (I mean, seriously, what was I on), but it feels like I'm skirting the line from Nobody into Somebody and man alive is it freaky.

[goulloynes]

some people on this website act as if polls are the pinnacle of professional data collecting

[sewingsillythings]

God I just want to bake bread and make clothes, and music, and write. But here I am writing 10 cover letters a day for a month just to get a job

[wokrfromhome]

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[mayra-quijotescx]

I grow weary of the app-ification of everything.

[bossjobs]

Netflix StartACareerToday - Work from Home Netflix

Work from Home Netfilx Jobs *Starting at $15 per hour *Applicant needed *Positions available

Aply here

[ausetkmt]

The New York Times: LinkedIn Ran Social Experiments on 20 Million Users Over Five Years

LinkedIn ran experiments on more than 20 million users over five years that, while intended to improve how the platform worked for members, could have affected some people’s livelihoods, according to a new study.

In experiments conducted around the world from 2015 to 2019, Linkedin randomly varied the proportion of weak and strong contacts suggested by its “People You May Know” algorithm — the company’s automated system for recommending new connections to its users. Researchers at LinkedIn, M.I.T., Stanford and Harvard Business School later analyzed aggregate data from the tests in a study published this month in the journal Science.

LinkedIn’s algorithmic experiments may come as a surprise to millions of people because the company did not inform users that the tests were underway.

Tech giants like LinkedIn, the world’s largest professional network, routinely run large-scale experiments in which they try out different versions of app features, web designs and algorithms on different people. The longstanding practice, called A/B testing, is intended to improve consumers’ experiences and keep them engaged, which helps the companies make money through premium membership fees or advertising. Users often have no idea that companies are running the tests on them. (The New York Times uses such tests to assess the wording of headlines and to make decisions about the products and features the company releases.)

But the changes made by LinkedIn are indicative of how such tweaks to widely used algorithms can become social engineering experiments with potentially life-altering consequences for many people. Experts who study the societal impacts of computing said conducting long, large-scale experiments on people that could affect their job prospects, in ways that are invisible to them, raised questions about industry transparency and research oversight.

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Share this article.“The findings suggest that some users had better access to job opportunities or a meaningful difference in access to job opportunities,” said Michael Zimmer, an associate professor of computer science and the director of the Center for Data, Ethics and Society at Marquette University. “These are the kind of long-term consequences that need to be contemplated when we think of the ethics of engaging in this kind of big data research.”

The study in Science tested an influential theory in sociology called “the strength of weak ties,” which maintains that people are more likely to gain employment and other opportunities through arms-length acquaintances than through close friends.

The researchers analyzed how LinkedIn’s algorithmic changes had affected users’ job mobility. They found that relatively weak social ties on LinkedIn proved twice as effective in securing employment as stronger social ties.

In a statement, Linkedin said during the study it had “acted consistently with” the company’s user agreement, privacy policy and member settings. The privacy policy notes that LinkedIn uses members’ personal data for research purposes. The statement added that the company used the latest, “non-invasive” social science techniques to answer important research questions “without any experimentation on members.”

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LinkedIn, which is owned by Microsoft, did not directly answer a question about how the company had considered the potential long-term consequences of its experiments on users’ employment and economic status. But the company said the research had not disproportionately advantaged some users.

The goal of the research was to “help people at scale,” said Karthik Rajkumar, an applied research scientist at LinkedIn who was one of the study’s co-authors. “No one was put at a disadvantage to find a job.”

Sinan Aral, a management and data science professor at M.I.T. who was the lead author of the study, said LinkedIn’s experiments were an effort to ensure that users had equal access to employment opportunities.

“To do an experiment on 20 million people and to then roll out a better algorithm for everyone’s jobs prospects as a result of the knowledge that you learn from that is what they are trying to do,” Professor Aral said, “rather than anointing some people to have social mobility and others to not.” (Professor Aral has conducted data analysis for The New York Times, and he received a research fellowship grant from Microsoft in 2010.)

Experiments on users by big internet companies have a checkered history. Eight years ago, a Facebook study describing how the social network had quietly manipulated what posts appeared in users’ News Feeds in order to analyze the spread of negative and positive emotions on its platform was published. The weeklong experiment, conducted on 689,003 users, quickly generated a backlash.

The Facebook study, whose authors included a researcher at the company and a professor at Cornell, contended that people had implicitly consented to the emotion manipulation experiment when they had signed up for Facebook. “All users agree prior to creating an account on Facebook,” the study said, “constituting informed consent for this research.”

Critics disagreed, with some assailing Facebook for having invaded people’s privacy while exploiting their moods and causing them emotional distress. Others maintained that the project had used an academic co-author to lend credibility to problematic corporate research practices.

Cornell later said its internal ethics board had not been required to review the project because Facebook had independently conducted the study and the professor, who had helped design the research, had not directly engaged in experiments on human subjects.

The LinkedIn professional networking experiments were different in intent, scope and scale. They were designed by Linkedin as part of the company’s continuing efforts to improve the relevance of its “People You May Know” algorithm, which suggests new connections to members.

The algorithm analyzes data like members’ employment history, job titles and ties to other users. Then it tries to gauge the likelihood that a LinkedIn member will send a friend invite to a suggested new connection as well as the likelihood of that new connection accepting the invite.

For the experiments, LinkedIn adjusted its algorithm to randomly vary the prevalence of strong and weak ties that the system recommended. The first wave of tests, conducted in 2015, “had over four million experimental subjects,” the study reported. The second wave of tests, conducted in 2019, involved more than 16 million people.

During the tests, people who clicked on the “People You May Know” tool and looked at recommendations were assigned to different algorithmic paths. Some of those “treatment variants,” as the study called them, caused LinkedIn users to form more connections to people with whom they had only weak social ties. Other tweaks caused people to form fewer connections with weak ties.

Whether most LinkedIn members understand that they could be subject to experiments that may affect their job opportunities is unknown.

LinkedIn’s privacy policy says the company may “use the personal data available to us” to research “workplace trends, such as jobs availability and skills needed for these jobs.” Its policy for outside researchers seeking to analyze company data clearly states that those researchers will not be able to “experiment or perform tests on our members.”

But neither policy explicitly informs consumers that LinkedIn itself may experiment or perform tests on its members.

In a statement, LinkedIn said, “We are transparent with our members through our research section of our user agreement.”

In an editorial statement, Science said, “It was our understanding, and that of the reviewers, that the experiments undertaken by LinkedIn operated under the guidelines of their user agreements.”

After the first wave of algorithmic testing, researchers at LinkedIn and M.I.T. hit upon the idea of analyzing the outcomes from those experiments to test the theory of the strength of weak ties. Although the decades-old theory had become a cornerstone of social science, it had not been rigorously proved in a large-scale prospective trial that randomly assigned people to social connections of different strengths.

The outside researchers analyzed aggregate data from LinkedIn. The study reported that people who received more recommendations for moderately weak contacts generally applied for and accepted more jobs — results that dovetailed with the weak-tie theory.

In fact, relatively weak contacts — that is, people with whom LinkedIn members shared only 10 mutual connections — proved much more productive for job hunting than stronger contacts with whom users shared more than 20 mutual connections, the study said.

A year after connecting on LinkedIn, people who had received more recommendations for moderately weak-tie contacts were twice as likely to land jobs at the companies where those acquaintances worked compared with other users who had received more recommendations for strong-tie connections.

“We find that these moderately weak ties are the best option for helping people find new jobs and much more so than stronger ties,” said Mr. Rajkumar, the Linkedin researcher.

The 20 million users involved in LinkedIn’s experiments created more than 2 billion new social connections and completed more than 70 million job applications that led to 600,000 new jobs, the study reported. Weak-tie connections proved most useful for job seekers in digital fields like artificial intelligence, while strong ties proved more useful for employment in industries that relied less on software, the study said.

LinkedIn said it had applied the findings about weak ties to several features including a new tool that notifies members when a first- or second-degree connection is hiring. But the company has not made study-related changes to its “People You May Know” feature.

Professor Aral of M.I.T. said the deeper significance of the study was that it showed the importance of powerful social networking algorithms — not just in amplifying problems like misinformation but also as fundamental indicators of economic conditions like employment and unemployment.

Catherine Flick, a senior researcher in computing and social responsibility at De Montfort University in Leicester, England, described the study as more of a corporate marketing exercise.

“The study has an inherent bias,” Dr. Flick said. “It shows that, if you want to get more jobs, you should be on LinkedIn more.”

[davidaugust]

So LinkedIn is automatically opting-in everyone to "use your personal data and content you create on LinkedIn to train generative AI models that create content." Jerk move on their part.

You can opt-out at this link: https://www.linkedin.com/mypreferences/d/settings/data-for-ai-improvement

You can delete your profile at this link (since their default opt-in suggests no respect for our personal data): https://www.linkedin.com/mypreferences/d/close-accounts

[scrapin1]

Efficient Data Extraction with LinkedIn Data Scraping Tools

LinkedIn data holds immense potential for businesses. A LinkedIn data extraction tool can help you harness this potential by extracting relevant data efficiently. This data can then be used to enhance various business functions. Additionally, automated tools can reduce manual effort and increase accuracy.

linkedin data extraction tool

[webscreen-scraping]

LinkedIn data scraper extracts the professional user as well as business profile pages. Scraping LinkedIn data can be manually done and it needs a larger amount of time, effort, and human resources. We scrape LinkedIn data within the timeline and make efficiently for you with our LinkedIn profile data scraper. Our LinkedIn data scraper assists in getting potential employees, job posting, and getting data about the recruitments through LinkedIn.

[juliebowie]

A Comprehensive Data Science Linkedin Profile Guide

Summary: Build a rockstar LinkedIn profile to attract recruiters & showcase your Data Science expertise. Learn how to craft a strong foundation, highlight skills & achievements, and optimise for job searches.

Introduction

In today's data-driven world, Data Scientists are in high demand. But landing your dream job requires more than just technical expertise. Your online presence, particularly your LinkedIn profile, plays a crucial role in showcasing your skills and attracting potential employers.

This comprehensive guide will equip you with the knowledge to create a LinkedIn profile that stands out from the crowd and propels your Data Science career forward.

Read More: How to Optimise Your LinkedIn as a Data Scientist

Understanding the Importance of LinkedIn for Data Scientists

LinkedIn is the go-to platform for professional networking and career development. It's a digital marketplace where recruiters actively search for qualified Data Science talent. Here's why having a strong LinkedIn profile is essential:

Visibility: A well-crafted profile increases your visibility to recruiters and hiring managers searching for Data Scientists.

Credibility: A professional profile with relevant skills and experience establishes you as a credible and qualified candidate.

Networking: LinkedIn allows you to connect with other Data Scientists, industry leaders, and potential employers, fostering valuable connections within the field.

Job Opportunities: Many companies post Data Science job openings directly on LinkedIn, giving you access to a wealth of opportunities.

Industry Insights: Engage with Data Science groups and discussions on LinkedIn to stay updated on the latest trends and innovations in the field.

Creating a Strong Profile Foundation

The foundation of your LinkedIn profile starts with a strong first impression. Here's what you need to get started:

Professional Headshot: Use a clear, high-quality headshot that portrays you in a professional and approachable manner.

Compelling Headline: Your headline is prime real estate, so make it count. Include your current job title, company name (if applicable), and relevant keywords like "Data Scientist" or "Machine Learning Engineer."

Clear Summary: Write a concise and engaging summary that highlights your experience, skills, and career goals. Quantify your achievements whenever possible and showcase your passion for Data Science.

Detailing Your Experience and Skills

Your experience section is where you showcase your Data Science expertise. Follow these tips to effectively present your past roles:

Quantify Your Impact: Don't just list your responsibilities; demonstrate the impact you made. Use metrics to showcase how your work improved efficiency, reduced costs, or yielded positive results.

Tailor to Each Role: Adapt your experience descriptions to highlight the skills and technologies relevant to each specific job you held.

Keywords: Integrate relevant keywords like "machine learning," "deep learning," "Data Analysis," "Python," or "R" throughout your experience section to improve discoverability in recruiter searches.

List Relevant Skills: Include a comprehensive list of hard skills relevant to Data Science like programming languages, machine learning algorithms, and data visualisation tools.

Soft Skills Matter: Don't underestimate the importance of soft skills like communication, problem-solving, and teamwork. Add these to your profile as well.

Endorsements: Encourage colleagues and connections to endorse your skills to add credibility to your profile.

Showcasing Your Work and Achievements

Your LinkedIn profile shouldn't just tell - it should show! Here are ways to showcase your work and achievements:

Projects and Publications: List any personal projects, research papers, or open-source contributions you have made. Include links to these projects whenever possible.

Awards and Recognitions: Highlight any awards, certifications, or recognitions you've received in the Data Science field.

Presentations and Talks: If you've presented your work at conferences or events, mention them in your profile and include links to recordings or presentations (if available).

Sharing Data Science Insights: Publish blog posts or articles on LinkedIn to demonstrate your thought leadership and expertise.

Building a Network and Engaging with the Community

LinkedIn isn't just a static profile; it's a platform for building connections and engagement. Here's how to foster a vibrant network:

Connect with Relevant People: Seek out connections with other Data Scientists, industry leaders, and companies you're interested in working for.

Join Groups and Discussions: Actively participate in Data Science groups on LinkedIn. Share your knowledge, engage in discussions, and build relationships with others in the field.

Follow Influencers: Follow Data Science thought leaders and companies to stay updated on the latest industry trends and news.

Optimizing Your Profile for Recruiters and Job Searches

While building your network is important, attracting recruiters is crucial too. Optimize your profile for discoverability:

Keywords Throughout: Strategically include relevant keywords throughout your profile, including your headline, summary, experience, and skills section.

Utilize the "Open to Work" Feature: Signal your availability to recruiters by enabling the "Open to Work" feature. You can choose to broadcast this publicly or target specific companies you're interested in.

Customize Your Profile URL: Replace the generic LinkedIn URL with a custom URL that includes your name. This makes your profile easier to find and share.

Conclusion

Crafting a compelling LinkedIn profile takes time and effort, but the rewards are substantial. By following these guidelines and consistently updating your profile, you will create a powerful online presence that attracts recruiters. Remember, your LinkedIn profile is a dynamic tool; keep it fresh, engaging, and reflective of your evolving skillset.

As you navigate your Data Science journey, leverage the power of LinkedIn to connect with the Data Science community, build meaningful relationships, and unlock exciting career opportunities.

With a strategic approach and a commitment to professional development, your LinkedIn profile can become the key that unlocks your dream Data Science career.

Frequently Asked Questions

I Don't Have a Lot of Experience Yet. Can I Still Build a Strong LinkedIn Profile?

Absolutely! Focus on showcasing your skills through projects, online courses, or volunteer work. Actively participate in Data Science groups and demonstrate your passion for the field.

What Are Some Keywords I Should Include in My Profile?

Integrate keywords relevant to Data Science like "machine learning," "deep learning," "Data Analysis," "Python," or "R" throughout your experience, skills, and summary sections.

How Can I Leverage LinkedIn to Network with Other Data Scientists?

Join Data Science groups and discussions, connect with relevant people in the field, and follow industry leaders. Share your knowledge, engage in conversations, and build valuable relationships.