Why social media algorithms send you posts you don’t like

Ziv Epstein, Massachusetts Institute of Technology (MIT); Farnaz Jahanbakhsh, University of Michigan, and Michael Bernstein, Stanford University

Image: Berke Citak - Unsplash

Do your social media accounts feed you content that reflects your core beliefs and guiding principles? Our new research published in the Proceedings of the National Academy of Sciences shows that the algorithms supplying your feeds may be prioritizing content that clashes with your values. That’s because the algorithms heavily weigh online posts that you reply to, and social media users tend to more often comment on content they take issue with than content they agree with.

Notably, our study of the X social media platform shows that although the X feed algorithm promotes content to both Democratic and Republican users that contradicts their values, it does so more extensively for Democrats.

How content gets into your feed

Social media platforms use powerful algorithms that select posts to display in your feed from a vast pool of possible content.

On X, for example, posts appear on your screen as “For You” pages. The algorithms predict the likelihood you will engage with the content – click a “like” icon or add a comment. Then they use the accuracy of those predictions to tailor what they serve you next time. Platforms use these interactions to learn their users’ tendencies.

But will the posts you receive reflect what you actually value? Some users care most about preserving traditions or keeping society safe. Others care more about free expression or protecting the natural world. Most people care about all of those things, to different extents. A feed aligned with a person’s values would reflect those varying priorities.

Psychologists use well-established surveys to measure what a person values. To measure values expressed in the posts a platform selects for someone, we built a measurement tool that uses standard psychological classifications of human values. We then applied it to the feeds of 715 U.S.-based users on X.

We discovered that the X feed algorithm is most likely to amplify posts about upholding tradition, following rules or keeping society safe. And it is most likely to demote posts about looking after people, concern for people far away, being dependable or protecting nature.

When we compared these values against the values users had expressed in their own posts, we found the algorithm was more likely to promote posts that conflict with users’ values than posts that align.

Why your feed may clash with your values

Why did this trend occur? First, we checked whether users follow accounts that diverge from their values to begin with, but we determined that most accounts people follow do, in fact, align.

We also looked at whether people engage only with posts they disagree with, in which case the algorithm would just be serving up more of the same. But we found that people engage with plenty of posts that reflect values they agree with.

The catch has to do with the nature of the interactions. People primarily respond to content by “liking” it – clicking a “heart” button on X or a “thumbs-up” button on Facebook. Less frequently, people will write a reply, and when they do, we find that they often reply to posts that clash with their values.

Here is the smoking gun: The X algorithm treats those rare replies as a much weightier signal than the many likes. Essentially, it learns most strongly from replies. As a result, the algorithm tends to send a user “For You” posts that reflect the values of posts they’ve commented on – which tend to clash with their own values.

Posts to Democrats are more objectionable

Now the twist: We found that this algorithmic tendency is stronger on X for users who reported to be Democrats than those who reported to be Republicans. Our evidence indicates that this is because Democrats object more than Republicans to content they reply to. That creates a stronger feedback loop in which the algorithm more strongly presents clashing posts. The content the algorithm amplifies is more than four times more misaligned for Democrats than it is for Republicans.

So what comes next? In other research, we’ve hit upon one way for social media platforms to better align feeds with users’ values. We created a way for platform designers to ask users what they value and to then sort their feeds accordingly. We found that users are quite good at distinguishing whether sample feeds sent to them align or do not align with their values.

Aligning feeds with values may open a possible door out of echo chambers in a way that unmediated exposure to the other side does not. Recent research from our team has shown that algorithms optimized for engagement – basically handing people the opposition and leaving them to sort it out – may even be responsible for more polarization, not less. Surfacing bridging content that spans political lines while speaking to what the user values could be a promising direction for fostering both user autonomy and constructive conversation.

Ideally, in our view, the people who use a social media platform should have a greater say in the kinds of information shown to them. If platform designers, the public and policymakers can create new tools that facilitate this goal, then perhaps platforms can better support the values and actions people care about.

Ziv Epstein, Postdoctoral Associate in Social and Ethical Responsibilities of Computing, Massachusetts Institute of Technology (MIT); Farnaz Jahanbakhsh, Assistant Professor of Electrical Engineering and Computer Science and of Information, University of Michigan, and Michael Bernstein, Professor of Computer Science, Stanford University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Fact-checked by Irfan Ahmad.

Read next: How attackers persuade AI agents to break the rules

by External Contributor via Digital Information World

How attackers persuade AI agents to break the rules

By  Tanya Petersen, EPFL

As AI assistants evolve into AI agents, a new EPFL study has found that the biggest safety risks in their use may not come from single malicious prompts, but from carefully orchestrated conversations.

Image: Roman Budnikov - Unsplash

Today, most of us interact with AI assistants - reactive bots that wait for human instructions. Yet, AI assistants are rapidly being replaced by agentic AI agents, that can interact with external tools, browse the web, generate images, send emails, and perform increasingly complex workflows on behalf of users.

As these agentic agents become more capable, they may be exploited by people with bad intentions. Safety tests have generally only checked whether an AI agent refuses a single harmful request - they don't measure what happens when an attacker gradually persuades the AI through a series of seemingly harmless conversations.

Now, researchers from EPFL’s Natural Language Processing Laboratory have developed STING(Sequential Testing of Illicit N-step Goal execution), an automated testing framework that simulates how a real attacker might manipulate large language model (LLM) agents into carrying out harmful tasks over multiple interactions.

In their paper, presented at the prestigious 2026 International Conference on Machine Learning, the EPFL researchers outline how, rather than simply asking an AI agent to do something obviously malicious, STING breaks an illicit objective into a series of seemingly harmless requests, each building towards the final goal.

"LLM agents are everywhere," says PhD student Ayush Kumar Tarun in the NLP Lab, and lead author of the study. "They're very powerful, but what if someone with bad intentions wants to use those same agents? That's what we wanted to understand."

As companies race to deploy these systems, ensuring they cannot be manipulated into assisting with cybercrime, fraud or other harmful activities has become an urgent challenge. In one example, META admitted in June that attackers used simple social engineering tactics, rather than malicious code or malware, to trick its AI support assistant into granting unauthorized access to Instagram accounts.

Thinking like an attacker

"If you simply ask an AI agent to hack someone's account, today's models are generally smart enough to refuse," explained Antoine Bosselut, head of the NLP Lab and co-author of the paper. "But, if you decompose that goal into smaller, more benign-looking requests, and adapt those requests as the conversation progresses, you have a much better chance of getting the agent to perform the actions you want.”

STING reproduces this behavior by creating an automated "attacker" that develops a step-by-step plan before attempting to persuade a target AI agent to execute each stage.

The researchers tested this approach across 176 harmful task scenarios involving several leading AI models (like GPT, Gemini, and Claude) operating as tool-using agents. The results showed that multi-turn attacks consistently succeeded more often than traditional single-prompt tests. In some cases, agents were twice as likely to complete harmful tasks when attackers gradually built towards their objective rather than stating it outright.

"We expected multi-turn attacks to perform better. What surprised us was the magnitude," says Tarun. "For some models, harmful task completion was around two times higher than with existing single-turn evaluations. STING measures how quickly an attack’s success occurs, rather than only whether an attack eventually succeeds, allowing different testing approaches to be compared more fairly.”

Challenging assumptions about language

The team also explored whether attacks became more effective in different, lower-resource languages where less training data exists, and were surprised by the results.

"We expected to see differences across languages because earlier research showed that translating prompts into lower-resource languages could bypass safety measures," says Tarun. "But for agents, we found that harmful task completion rates were remarkably similar across all seven languages we tested."

That finding challenges a growing assumption in AI safety research that multilingual vulnerabilities naturally increase as language resources decrease. However, the researchers also discovered an important caveat. When sophisticated attackers switched languages during different stages of a multi-step attack, harmful task completion could become dramatically more successful, highlighting another avenue that future safety evaluations should examine.

Safety shouldn't be an afterthought

The research comes at a time when the rapid adoption of agentic AI agents makes proactive safety testing essential.

"We've focused enormously on making agents more capable," says Bosselut. "But we also need people working on how to prevent those capabilities from being misused. Traditionally, safety has often been something people think about when something breaks, not before, but with AI agents, it can't be an add-on or an afterthought."

Looking ahead, the research team hopes STING will encourage developers to embed safety testing much earlier in the design process and, develop a framework for multi-agent systems.

“Research efforts to expose vulnerabilities are much larger in number than those showing practical defense strategies, and this is a crucial area of development. We also need to address what we can do for the models that are already out there in the wild for which safety was a post-hoc addition,” concluded Tarun.

This content is republished under the CC BY-SA 4.0 license.

Reviewed by  Irfan Ahmad.

Read next: Offloading work tasks to AI comes with a cost – to our brains
by External Contributor via Digital Information World

Offloading work tasks to AI comes with a cost – to our brains

Jongkil Jay Jeong, The University of Melbourne; RMIT University; Deakin University

Image: For illustration purposes, created by DIW with GenAI tools.

Imagine your team has been tasked to deliver a high-stakes policy paper under intense time pressure. Everyone turns to generative artificial intelligence (AI) and within minutes, it delivers a full draft complete with structured arguments and authoritative-looking citations.

The team then transfers the AI-generated text into the corporate template, polishes the narrative flow and structure, and gives it one final check before sending it up the chain. Leadership takes a glimpse of the polished-looking draft, and – assuming the underlying research has already been verified – signs off and publishes.

This scenario is not hypothetical. South Africa’s Draft National AI Policy had to be withdrawn earlier this year after reviewers found a number of the citations pointed to journal articles and authors that didn’t exist. Ironically, the mistakes were introduced by AI.

Similarly, last year, consultancy firm Deloitte provided a partial refund to the Australian government after admitting that generative AI had been used to help produce a commissioned document which contained fabricated citations and referenced quotes from sources that simply did not exist. The report cost Australian taxpayers A$440,000.

But the cost of letting AI do our work and thinking also comes at a cost to our brain.

Value of thought

As seen in the examples above, work tasks produced by AI often look polished on the surface but carry significant underlying flaws. This is a byproduct of how the modern workplace has conditioned us to prioritise the final deliverable – the report, the presentation, the assignment – over the actual process.

However, this strictly outcome-driven approach rarely rewards the process by which results are obtained. When we skip the process, we lose the ability to fully grasp what the output actually means.

This will inevitably result in us finding it challenging to critically evaluate the outcomes we consume. More importantly, it will also impact our capacity to discern facts from partial truths and lies.

Ultimately, the true value of knowledge work lies not just in the final deliverable, but in the clarity of thought and the messy, iterative process required to achieve it.

Blindly relying on AI

Recent empirical evidence also highlights a severe disconnect between perceived benefits of using AI and its improvement in quality.

A 2025 study by the University of Melbourne and accounting firm KPMG surveyed more than 48,000 respondents across 47 countries. It found two in three people (66%) use AI on a regular basis, while more than half believe their performance benefits from its usage.

The study specified that the primary driver of AI adoption was the fear of missing out (48%), leading users to prioritise the speed of delivery over the quality of the final output.

Yet 61% of respondents said they have had no AI training, while 60% also reported inappropriate, complacent and non-transparent use of AI in their workplace.

A separate study from July 2026 by the Centre of AI Safety, a San Francisco-based nonprofit research organisation, found even the top-performing AI agents failed to complete roughly 85% of projects to a standard acceptable for commissioned work.

This indicates that even today’s best AI models still fall short of professional quality on most projects. It also shows we are relying on AI to generate final deliverables without understanding how it gets there.

This blind reliance strips away quality control, replacing genuine knowledge creation with an ever-increasing volume of automated “work slop”.

Improving cognitive skills

So how do we reverse this trend?

The answer isn’t simply banning or limiting AI in the workplace. Instead, we need to redefine our working relationship with AI and the outcomes it produces by taking a more human-centric approach.

First, organisations must set realistic time frames for deliverables that genuinely allow for people to think rather than focus on doing things quicker. There is a need to actively disconnect from AI tools during the brainstorming and structuring phases so ideas and thoughts are driven by actual human synthesis, not just algorithmic prediction.

Traditional whiteboard sessions – where teams physically map out their initial concepts through markers and pens – is a way this may be facilitated.

Second, we must cultivate a culture that is constructively critical of all outputs, whether generated by humans or AI.

This means actively questioning logic, structure, references and underlying assumptions. When teams are expected to defend the behavioural reasoning and methodology behind their work, it very quickly reveals who actually did the thinking and who merely copy-pasted from an AI agent.

Perhaps creating a critique group can create a safe, comfortable environment where feedback and comments can flow freely.

Technology can assist with certain processes, but it is dangerous to assume it can replace our ability to think critically. This is because the most valuable asset we have isn’t the polished final page – it’s the ebbs and flows of the cognitive process that is required to honestly write and evaluate one.

Jongkil Jay Jeong, Senior fellow, The University of Melbourne; RMIT University; Deakin University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Fact checked by Irfan Ahmad.

Editor’s Note: This article was reviewed and found to be from a credible source, researcher-authored, and of good content quality.

Read next: Research Examines World War II’s Lasting Impact on Energy, Resources and Environment

by External Contributor via Digital Information World

Research Examines World War II’s Lasting Impact on Energy, Resources and Environment

By Inderscience

Research in the International Journal of Sustainable Development has looked at eight of the nations involved in the Second World War, both Allied and Axis countries, and shows how that period of history helped set societies on the path to the resource-intensive modern economies we have today. The research links wartime mobilisation to the period known as the Great Acceleration in energy use, material consumption, and ultimately detrimental environmental impact.

The study looks at societal change from before the war, 1935, to the post-war recovery period and the boomer years up to 1960. Demographics, economic activity, power supply, material and resource flow, and environmental impact are all examined. Three major consequences of WWII are seen. First, acceleration, in which existing trends become even more intense. Secondly, redirection where development shifts towards new technologies and the opening up of novel resources. Thirdly, reset, in which destruction or political upheaval changed the direction of nations from the paths there were on before the war.

The researchers use the term "socio-metabolic transition" to describe the various changes in power consumption and physical resources in society. By adopting this almost biological model, they were able to connect wartime production and resource mobilisation with institutional and technological changes that persisted long after 1945.

Abrari, L., Rezaei, N. and Linnanen, L. (2026) 'World War II and its lasting legacy: an overview of socio-metabolic transition, environmental impacts and resource flows', Int. J. Sustainable Development, Vol. 29, No. 5, pp.1–62. DOI: 10.1504/IJSD.2026.155697

Image: Austrian National Library - Unsplash

Reviewed by Irfan Ahmad.

Read next: AI bias isn’t just an error in the algorithm. It’s a chain of human decisions
by External Contributor via Digital Information World

AI bias isn’t just an error in the algorithm. It’s a chain of human decisions

Muneera Bano, CSIRO and Didar Zowghi, CSIRO

Image: Google DeepMind - Unsplash

In the United States, leading HR software company Workday is currently facing a lawsuit over its use of job screening tools powered by AI which allegedly discriminated against applicants based on factors such as age, disability and race.

The company, whose hiring software is widely used by large employers around the world, has denied the allegations.

The case is one of many examples of AI systems alleged to have caused discriminatory harm. When AI systems replicate and amplify discrimination, they blur the boundary between technical error and systemic injustice, turning bias into a digital harm.

And while our first instinct might be to blame the algorithms, they don’t decide what they can generate, what safeguards are built into them, or how a company responds when incidents of discrimination are reported. People make those calls long before an AI produces any output.

That is why technical fixes to AI systems are not enough. What is needed is an overhaul of AI ecosystems to ensure they are more inclusive.

A broader pattern

AI systems quietly narrow who gets seen as competent, employable or fit to lead.

For example, in a 2025 study, we tested how two AI models, OpenAI’s GPT-4 (which has now been retired) and Microsoft Copilot, represented software engineers in a simulated recruitment exercise: 300 candidate profiles for four job roles, followed by recommendations and generated images of each AI model’s preferred candidates.

Both models favoured male profiles, especially for senior roles. Their images also skewed towards engineers who were younger, slimmer, and lighter-skinned. The models were reproducing associations embedded in language, imagery, employment records and assumptions about who belongs in the profession.

These outputs don’t stay contained to a research study. AI-generated recommendations are entering hiring, education and public services.

This matters when certain demographics and women remain underrepresented in AI development and leadership, while being disproportionately exposed to its harms.

The problem is not limited to gender and race.

Even when AI systems operate across different languages and cultures, they often reproduce predominantly western values, assumptions and ways of understanding the world. The wealthy countries have become the main beneficiaries of AI, which widens global inequality.

In another study from 2025, we manually reviewed reported AI incidents.

Almost half involved a diversity or inclusion issue, with racial, gender and age discrimination most prominent. The harms traced back to different points in the AI development lifecycle: non diverse training data, and neglected diversity and inclusion principles during design, development and deployment.

Why technical fixes are not enough

Technical work matters, including bias identification, re-balancing datasets and adjusting outputs. But these fixes often treat bias as a property of the AI model, when much of it originates from outside the system.

Data does not enter an AI system as a neutral record of reality.

People decide what data to collect, how to label and categorise it, and whose experiences are important. These decisions are shaped by history, cultural norms, institutions and existing power imbalances.

Wherever society has linked leadership with men, technical skill with lighter skin, or innovation with youth, AI models learn from those associations and formalise, automate, and repeat them at a larger scale.

Bias also usually appears through the intersection of multiple identities, such as gender, race, age, disability and class. A system that looks fair when each identity is tested separately can still disadvantage people at the overlap of several.

Building a more inclusive AI ecosystem

That’s why building a more inclusive AI ecosystem requires interdisciplinary knowledge, such as educating AI engineers about social science theories to help them understand the social origin of bias.

Inclusive AI is not about political correctness; it is about upholding human rights, preventing harm, ensuring justice, and building trust.

It also requires genuine participation from affected groups and sustained attention to the power structures these systems operate within. AI development teams should test not just whether a model is accurate, but whether its benefits, errors and harms distribute fairly across different groups.

Together, this would help ensure tech companies better understand the nature of a bias once it’s manifested through AI and therefore develop new methods or tools to minimise the harm it causes.

Organisations that adopt AI also need stronger governance to monitor how the technology behaves. This could include, for example, having someone accountable for reviewing risk and responding to incidents and monitoring systems once they are live.

Algorithms don’t decide which data matter or what level of risk is acceptable. People make these choices. It’s high time tech companies remember that. The focus should not just be on fixing a biased algorithm, but rather on examining the human decisions that allowed the risk of harm, and who was missing when those decisions were made.

Muneera Bano, Principal Research Scientist, CSIRO and Didar Zowghi, Professor, Senior Principal Research Scientist, CSIRO

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Fact-Checked by Irfan Ahmad.

Editor’s Note: This article was reviewed and found to be from a credible source, researcher-authored, and of good content quality.

Read next:

• Artificial intelligence acts as an ‘ideological chameleon’ and may deepen political polarization, study finds

• The AI Skill Employers Value Most Might Be Human

by External Contributor via Digital Information World

The AI Skill Employers Value Most Might Be Human

By Ellie Stewart

Job seekers looking to increase their AI skillset in order to land their next role may be building AI skills that hiring professionals aren’t necessarily looking for.

New research by Adobe Acrobat reveals that there is a gap between the skills related to AI that people are acquiring and those that employers really value. Moreover, the idea that people should get more technical knowledge about AI appears to overlook the value of soft skills like judgment, teamwork, and critical thinking.

According to the Adobe data, the most popular technical skill among job seekers was brainstorming using AI, which accounted for 30% of surveyed job seekers. Then comes AI image generation and workflow automation, 19% each. But only 45% of people reported feeling comfortable with their skills to write prompts.

When it comes to recruiters, understanding where this gap exists can be important for hiring the best talent.

The new AI skillset expands beyond the prompt

Hiring managers prioritized time management above all other soft skills, with 47% selecting it, followed by adaptive problem solving at 44% and collaboration at 41%.

As for ethical AI supervision, it was chosen by 33% of hiring managers, whereas creative intuition was prioritized by 28%. Especially notable is that hiring managers prioritized ethical AI supervision 74% more often than prompt engineering.

This difference may acquire more significance as the availability of generative AI tools decreases its value.

In case almost everyone can ask the AI system to generate the picture, summarize a document or come up with some ideas, building a skillset that not only utilizes AI tools but thoughtfully partners with them may be what helps a job seeker stand out from the crowd.

According to the Adobe data, the biggest red flag related to AI in an interview process was a candidate's inability to check facts, cited by 70% of hiring professionals

Also, nearly half thought that taking full credit for work done with AI help is something to be worried about.

AI fluency is becoming a process, not a software skill

The current working landscape has changed what it means to be skilled with AI tools.

The first generation of workplace AI skills was based on usage and execution. Could an employee use a chatbot? Could he create an image? Did she understand prompting?

These execution based skills now serve as a foundation for a larger skillset.

The most ideal candidates would show their understanding of how to analyze outputs, fix mistakes and pick tools, as well as build processes for using AI. This means that being able to explain the reasoning behind the use of an AI tool and what to do next is just as important as using the tool itself.

This may also change the tools employees choose to use. While many believe the best Ai tools to be those that generate the most output, there are also tools that can provide meaningful assistance throughout other steps of a project’s process. These tools may allow users to better optimize their work and bridge the gap between their skills and the tools’ capabilities.

This illustrates a much more teamwork-based approach to AI tool use, where the user leans on technology as a creative partner but allows for increased responsibility for the final product.

Employers have a skills gap of their own

It is not the sole responsibility of employees to bridge the gap.

According to Adobe, over 90% of hiring managers stated that their organization offered training in the use of AI skills; however, knowledge sharing remains inconsistent. Even when looking at technology companies, only one out of three companies has a formal system in place to share knowledge of AI.

Moreover, the training is primarily focused on efficiency. Workflow automation was the most prevalent AI skill taught, accounting for 52% of courses offered, followed by AI brainstorming (48%) and prompt engineering (32%). AI output auditing, even though employers pay so much attention to this aspect, accounts for up only 30%.

This leaves businesses with their own challenge.

Organizations, if they want employees to use AI responsibly, question its outputs and have a good judgment regarding AI outputs, should train employees on such matters rather than offer them access to more tools.

The most valuable AI skill might be knowing what humans still need to do

The suggestion that technical understanding of AI is irrelevant does not hold much water.

According to the Adobe survey, nearly three in five hiring professionals state that AI mastery improves an employee’s job security. However, over 60% also agreed that they may still hire a candidate without basic AI proficiency.

This means that AI skill building in the workplace may be changing directions. The optimal AI user at work is someone who is capable of using AI technologies in a way that compliments their human skills, especially when it comes to decision making.

For job seekers, this means highlighting your ability to implement AI tools and explaining the thought process behind your work. For organizations working towards future workplace solutions, it means utilizing AI tools where they best support your workers and enhance their own skillsets.

The survey included 805 job seekers and 406 hiring professionals, with fieldwork conducted in late April 2026.

Reviewed by Irfan Ahmad.

Read next: Online conflict may strengthen, not weaken, social movements, McGill researchers find
by Guest Contributor via Digital Information World

Online conflict may strengthen, not weaken, social movements, McGill researchers find

By McGill University

Study on the 2019 global climate strikes shows how strangers can discover shared narratives and mobilize together.

Image: Chris Sansbury - Unsplash

Online conflict tends to advance social movements rather than undermine them, according to new research from McGill's Desautels Faculty of Management.

“Anyone who has watched an online debate turn heated assumes arguing kills collective action,” said Emmanuelle Vaast, Professor of Information Systems and the study's senior author. “Our study shows the opposite: the same social media dynamics that produce conflict also enable millions of strangers to discover they are telling the same story and act together. It explains how a teenager’s solo school strike could become a worldwide mobilization in a matter of months.”

The study was carried out in collaboration with colleagues at Northeastern University and the University of Massachusetts.

The researchers examined social media activity related to the 2019 global climate strikes sparked by Swedish activist Greta Thunberg – the largest climate mobilization in history – to understand how distinct causes, from pipeline opposition to the protection of monarch butterflies and wolves, fused into a single global “movement of movements.” They found that this process happens in three phases: exposure (movements discover each other’s issues), entanglement (conversations become tangled and contentious) and coalescence (participants converge on shared goals and joint action).

“Conflict did not weaken the movement,” Vaast said. “Co-operation and disagreement evolved together, like the two strands of a double helix. Arguing was part of how a shared agenda took shape.”

Previous research has mostly examined single movements and treated online conflict as a threat to collective action, the researchers said.

Nearly two million Twitter posts studied

The team used computational text-analysis methods to analyze about 1.9 million Twitter (now called X) messages posted between April and September 2019. Vaast noted that by combining semantic network analysis and natural language processing at this scale, the team could observe movement dynamics and identify patterns that may have been obscured by other methodologies, such as interviews or case studies.

Although social media platforms have shifted since 2019 from largely chronological feeds to recommendation-driven ones optimized to hold users' attention, the researchers said this does not necessarily impede movement building.

“Recommendation algorithms can actually accelerate discovery. For instance, TikTok surfaces [social] causes to people who never sought them out. So, coming together on social media is still possible today. It just runs through more opaque mechanisms,” Vaast said.

“That's exactly why the 2019 moment is worth studying: it's a benchmark for what today's algorithms are enabling.”

About this study

“Entangling and Coalescing on Social Media: A Study of Issue Boundary Spanning in Movement of Movements,” by Carol Lee (Northeastern University), Pratyush Bharati (University of Massachusetts) and Emmanuelle Vaast (McGill), was published in Information Systems Journal.

Reviewed by Irfan Ahmad.

Read next: Being Social Is Not a Priority on Social Media These Days
by External Contributor via Digital Information World