Social Media Research Tool Can Reduce Polarization It Could Also Lead

A web-based method was shown to mitigate political polarization on X by nudging antidemocratic and extremely negative partisan posts lower in a user’s feed. The tool, which is independent of the platform, has the potential to give users more say over what they see on social media.iStock A new tool shows it is possible to turn down the partisan rancor in an X feed — without removing political posts and without the direct cooperation of the platform. The study, from researchers at the University of Washington, Stanford University and Northeastern University, also indicates that it may one day be possible to let users take control of their social media algorithms. The researchers created a seamless, web-based tool that reorders content to move posts lower in a user’s feed when they contain antidemocratic attitudes and partisan animosity, such as advocating for violence or jailing supporters... Researchers published their findings Nov.

27 in Science. Assistant Professor of Computer Science, Johns Hopkins University This research was partially supported by a Hoffman-Yee grant from the Stanford Institute for Human-Centered Artificial Intelligence. Reducing the visibility of polarizing content in social media feeds can measurably lower partisan animosity. To come up with this finding, my colleagues and I developed a method that let us alter the ranking of people’s feeds, previously something only the social media companies could do. Reranking social media feeds to reduce exposure to posts expressing anti-democratic attitudes and partisan animosity affected people’s emotions and their views of people with opposing political views.

I’m a computer scientist who studies social computing, artificial intelligence and the web. Because only social media platforms can modify their algorithms, we developed and released an open-source web tool that allowed us to rerank the feeds of consenting participants on X, formerly Twitter, in real time. A new Stanford-led study is challenging the idea that political toxicity is simply an unavoidable element of online culture. Instead, the research suggests that the political toxicity many users encounter on social media is a design choice that can be reversed. Researchers have unveiled a browser-based tool that can cool the political temperature of an X feed by quietly downranking hostile or antidemocratic posts. Remarkably, this can occur without requiring any deletions, bans, or cooperation from X itself.

The study offers the takeaway that algorithmic interventions can meaningfully reduce partisan animosity while still preserving political speech. It also advances a growing movement advocating user control over platform ranking systems and the algorithms that shape what they see, which were traditionally guarded as proprietary, opaque, and mainly optimized for engagement rather... The research tool was built by a multidisciplinary team across Stanford, Northeastern University, and the University of Washington, composed of computer scientists, psychologists, communication scholars, and information scientists. Their goal in the experiment was to counter the engagement-driven amplification of divisive content that tends to reward outrage, conflict, and emotionally charged posts, without silencing political speech. Using a large language model, the tool analyzes posts in real time and identifies several categories of harmful political subject matter, including calls for political violence, attacks on democratic norms, and extreme hostility toward... When the system flags such content, it simply pushes those posts lower in the feed so they are less noticeable, like seating your argumentative uncle at the far end of the table during the...

New research shows the impact that social media algorithms can have on partisan political feelings, using a new tool that hijacks the way platforms rank content. How much does someone’s social media algorithm really affect how they feel about a political party, whether it’s one they identify with or one they feel negatively about? Until now, the answer has escaped researchers because they’ve had to rely on the cooperation of social media platforms. New, intercollegiate research published Nov. 27 in Science, co-led by Northeastern University researcher Chenyan Jia, sidesteps this issue by installing an extension on consenting participants’ browsers that automatically reranks the posts those users see, in real time and still... Jia and her team discovered that after one week, users’ feelings toward the opposing party shifted by about two points — an effect normally seen over three years — revealing algorithms’ strong influence on...

Get the latest updates delivered to your inbox every day, and stay up-to-date for free 🧠📈 Get the latest updates delivered to your inbox every day, and stay up-to-date for free 🧠📈 Reranking partisan animosity in algorithmic social media feeds alters affective polarization Social media algorithms profoundly impact our lives: They curate what we see (1) in ways that can shape our opinions (2-4), moods (5-7), and actions (8-12). Because of the power that these ranking algorithms have to direct our attention, the research literature has articulated many theories and results detailing the impact that ranking algorithms have on us (13-17). However, validating these theories and results has remained extremely difficult because the ranking algorithm behavior is determined by the social media platforms, and only the platforms themselves can test alternative feed designs and causally...

Platforms, however, face political and financial pressures that constrain the kinds of experiments they can launch and share (18). Concerns about lawsuits and the need to preserve engagement-driven revenue further limit what platforms are willing to test, leaving massive gaps in the design space of ranking algorithms that have been explored in naturalistic... To address this gap, we present an approach that enables researchers to rerank participants' social media feeds in real time as they browse, without requiring platform permission or cooperation. We built a browser extension, a small add-on to a web browser that modifies how web pages appear or behave, similar to an ad blocker. Our extension intercepts and modifies X's web-based feed in real time and reranks the feed using large language model (LLM)-based rescoring, with only a negligible increase in page load time. This web extension allows us to rerank content according to experimentally controlled conditions.

The design opens a new paradigm for algorithmic experimentation: It provides external researchers with a tool for conducting independent field experiments and evaluating the causal effects of algorithmic content curation on user attitudes and... This capability allowed us to investigate a pressing question: Can feed algorithms cause affective polarization, i.e., hostility toward opposing political parties (19-22)? This concern has grown since the 2016 US presidential election (23), and the debate remains ongoing after the 2020 and 2024 elections. If social media algorithms are causing affective polarization, they might not only bear responsibility for rising political incivility online (24), they might also pose a risk to trust in democratic institutions (25). In this case, isolating the algorithmic design choices that cause polarization could offer alternative algorithmic approaches (26). A major hypothesized mechanism for how feed algorithms cause polarization is a self-reinforcing engagement loop: users engage with content aligning with their political views, the feed algorithm interprets this engagement as a positive signal,...

Some studies support this hypothesis, finding that online interactions exacerbate polarization (27), potentially because of the increased visibility of hostile political discussions (28), divisive language (29-33), and content that reinforces existing beliefs (34). However, large-scale field experiments aimed at reducing polarization by intervening on the feed algorithm -- for example, by increasing exposure to out-party content -- have found both a decrease (35) and an increase (36)... Similarly, recent large-scale experiments on Facebook and Instagram found no evidence that reduced exposure to in-party sources or a simpler reverse-chronological algorithm affected polarization and political attitudes (23, 37) during the 2020 US election. These mixed results reveal the difficulty in identifying what, if any, algorithmic intervention might help reduce polarization, especially during politically charged times. We distilled the goals of these prior interventions to a direct hypothesis that we could operationalize through real-time LLM reranking: that feed algorithms cause affective polarization by exposing users specifically to content that polarizes. An algorithm that up-ranks content reflecting genuine political dialogue is less likely to polarize than one that up-ranks demagoguery.

This content-focused hypothesis has been difficult to operationalize into interventions, making studies that intervene on cross-partisan exposure and reverse-chronological ranking attractive but more diffuse in their impact and thus more likely to observe mixed... However, by connecting our real-time reranking infrastructure with recent advances in LLMs, we could create a ranking intervention that more directly targets the focal hypothesis (38) without needing platform collaboration. We drew, in particular, on a recent large-scale field experiment that articulated eight categories of antidemocratic attitudes and partisan animosity as bipartisan threats to the healthy functioning of democracy (39). We operationalized these eight categories into an artificial intelligence (AI) classifier that labels expressions of these constructs in social media posts, does so with accuracy comparable to trained annotators, and produces depolarization effects in... This real-time classification enabled us to perform a scalable, content-based reranking experiment on participants' own feeds in the field (41). We conducted a preregistered field experiment on X, the most used social media platform for political discourse in the US (42), using our extension to dynamically rerank participants' social media content by either increasing...

The experiment was conducted during a pivotal moment in the 2024 US election cycle, from July to August 2024, an important period for understanding how social media feeds impact affective polarization. Major political events during the study period included the attempted assassination of Donald Trump, the withdrawal of Joe Biden from the 2024 presidential race, and the nomination of Kamala Harris as the Democratic Party's... These events allow us to examine the impact of heterogeneous AAPA content on partisan polarization and hostility. We measured the intervention's effect on affective polarization (43) and emotional experience (44). Compared with control conditions that did not rerank the feed, decreased AAPA exposure led to warmer feelings toward the political outgroup, whereas increased AAPA exposure led to colder feelings. These changes also affected participants' levels of negative emotions (anger and sadness) as measured through in-feed surveys.

Social media research tool lowers the political temperature A study shows that the order in which platforms like X display content to their users affects their animosity towards other ideological groups A team of U.S. researchers has shown that the order in which political messages are displayed on social media platforms does affect polarization — one of the most debated issues since the rise of social media and the... The phenomenon is equally strong regardless of the user’s political orientation, the academics note in an article published on Thursday in Science. Social media is an important source of political information.

For hundreds of millions of people worldwide, it is even the main channel for political engagement: they receive political content, share it, and express their opinions through these platforms. Given the relevance of social media in this sphere, understanding how the algorithms that operate on these platforms work is crucial — but opacity is the norm in the industry. That makes it extremely difficult to estimate the extent to which the selection of highlighted content shapes users’ political views. How did the researchers overcome algorithmic opacity to alter the order of posts that social media users see? Tiziano Piccardi from Stanford University and his colleagues developed a browser extension that intercepts and reorders the feed (the chronological timeline of posts) of certain social networks in real time. The tool uses a large language model (LLM) to assign a score to each piece of content, measuring the extent to which it contained “antidemocratic attitudes and partisan animosity” (AAPA).

Once scored, the posts were reordered one way or another — without any collaboration from the platform or reliance on its algorithm. The experiment involved 1,256 participants, who had all been duly informed. The study focused on X, as it is the social network most used in the U.S. for expressing political opinions, and it was conducted during the weeks leading up to the 2024 presidential election to ensure a high circulation of political messages. A new research tool has demonstrated the potential to reduce political polarization on the X platform without eliminating political content or requiring direct engagement from the platform itself. This development suggests a pathway for users to exert greater control over their social media feeds, particularly in politically charged environments.

The tool, developed by a team of researchers, employs algorithms designed to modify the visibility of partisan content. By adjusting the way posts are displayed, the tool can create a more balanced feed that minimizes extreme political viewpoints. This approach could significantly impact user engagement levels, fostering a healthier online discourse. The innovative tool allows users to tailor their experience based on their preferences. Users can choose to limit the visibility of highly partisan content without completely removing political discussions. This flexibility is crucial in promoting a more civil online atmosphere while still allowing for diverse political expression.

According to the research team, the tool does not depend on the cooperation of the X platform, which has often faced criticism for enabling divisive content. Instead, it empowers users to take charge of their feeds, potentially leading to a more thoughtful and constructive exchange of ideas. While the specific algorithms used in this tool have not been disclosed, the researchers emphasize that they can be adapted to accommodate various user preferences. This adaptability is central to its design, allowing for a tailored experience that aligns with individual values and engagement desires.

People Also Search

• Social media research tool can reduce polarization — it could also lead ...
• Social media research tool lowers the political temperature
• Down-ranking polarizing content lowers emotional temperature on social ...
• New Algorithmic Tool Shows Social Media Polarization Isn't Inevitable
• How Does Social Media Impact Political Polarization?
• Stanford Researchers Develop Browser Tool to Combat Political ...
• Algorithms do widen the divide: Social media feeds shape political ...
• Platform-independent experiments on social media | Science
• New Social Media Tool Aims to Reduce Political Polarization
• Role of (Social) Media in Political Polarization: A Systematic Review ...