The Prosocial Ranking Challenge: Reducing Polarization on Social Media without Sacrificing Engagement
- URL: http://arxiv.org/abs/2603.19626v1
- Date: Fri, 20 Mar 2026 04:10:56 GMT
- Title: The Prosocial Ranking Challenge: Reducing Polarization on Social Media without Sacrificing Engagement
- Abstract summary: We used a browser extension to modify which posts were shown to desktop social media users.<n>We randomly assigned 9,386 users to a control group or one of five alternative ranking algorithms.<n>We found no effects on well-being, news knowledge, outgroup empathy, perceptions of and support for partisan violence.
- Score: 37.62581855018157
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We report the first direct comparisons of multiple alternative social media algorithms on multiple platforms on outcomes of societal interest. We used a browser extension to modify which posts were shown to desktop social media users, randomly assigning 9,386 users to a control group or one of five alternative ranking algorithms which simultaneously altered content across three platforms for six months during the US 2024 presidential election. This reduced our preregistered index of affective polarization by an average of 0.03 standard deviations (p < 0.05), including a 1.5 degree decrease in differences between the 100 point inparty and outparty feeling thermometers. We saw reductions in active use time for Facebook (-0.37 min/day) and Reddit (-0.2 min/day), but an increase of 0.32 min/day (p < 0.01) for X/Twitter. We saw an increase in reports of negative social media experiences but found no effects on well-being, news knowledge, outgroup empathy, perceptions of and support for partisan violence. This implies that bridging content can improve some societal outcomes without necessarily conflicting with the engagement-driven business model of social media.
Related papers
- Contrastive Learning for Implicit Social Factors in Social Media Popularity Prediction [12.798988947331596]
We study factors introduced by social platforms that impact post popularity.
These factors include content relevance, user influence similarity, and user identity.
We propose three implicit social factors related to popularity, including content relevance, user influence similarity, and user identity.
arXiv Detail & Related papers (2024-10-12T03:25:11Z) - Dynamics of Ideological Biases of Social Media Users [0.0]
We show that the evolution of online platform-wide opinion groups is driven by the desire to hold popular opinions.
We focus on two social media: Twitter and Parler, on which we tracked the political biases of their users.
arXiv Detail & Related papers (2023-09-27T19:39:07Z) - Trust and Believe -- Should We? Evaluating the Trustworthiness of
Twitter Users [5.695742189917657]
Fake news on social media is a major problem with far-reaching negative repercussions on both individuals and society.
In this work, we create a model through which we hope to offer a solution that will instill trust in social network communities.
Our model analyses the behaviour of 50,000 politicians on Twitter and assigns an influence score for each evaluated user.
arXiv Detail & Related papers (2022-10-27T06:57:19Z) - Fine-tuned Sentiment Analysis of COVID-19 Vaccine-Related Social Media
Data: Comparative Study [0.7874708385247353]
This study investigated and compared public sentiment related to COVID-19 vaccines expressed on two popular social media platforms, Reddit and Twitter.
We created a fine-tuned DistilRoBERTa model to predict sentiments of approximately 9.5 million Tweets and 70 thousand Reddit comments.
Our results determined that the average sentiment expressed on Twitter was more negative (52% positive) than positive.
arXiv Detail & Related papers (2022-10-17T16:22:18Z) - Shifting Polarization and Twitter News Influencers between two U.S.
Presidential Elections [92.33485580547801]
We analyze the change of polarization between the 2016 and 2020 U.S. presidential elections.
Most of the top influencers were affiliated with media organizations during both elections.
75% of the top influencers in 2020 were not present in 2016, demonstrating that such status is difficult to retain.
arXiv Detail & Related papers (2021-11-03T20:08:54Z) - News consumption and social media regulations policy [70.31753171707005]
We analyze two social media that enforced opposite moderation methods, Twitter and Gab, to assess the interplay between news consumption and content regulation.
Our results show that the presence of moderation pursued by Twitter produces a significant reduction of questionable content.
The lack of clear regulation on Gab results in the tendency of the user to engage with both types of content, showing a slight preference for the questionable ones which may account for a dissing/endorsement behavior.
arXiv Detail & Related papers (2021-06-07T19:26:32Z) - Causal Understanding of Fake News Dissemination on Social Media [50.4854427067898]
We argue that it is critical to understand what user attributes potentially cause users to share fake news.
In fake news dissemination, confounders can be characterized by fake news sharing behavior that inherently relates to user attributes and online activities.
We propose a principled approach to alleviating selection bias in fake news dissemination.
arXiv Detail & Related papers (2020-10-20T19:37:04Z) - Breaking the Communities: Characterizing community changing users using
text mining and graph machine learning on Twitter [0.0]
We study users who break their community on Twitter using natural language processing techniques and graph machine learning algorithms.
We collected 9 million Twitter messages from 1.5 million users and constructed the retweet networks.
We present a machine learning framework for social media users classification which detects "community breakers"
arXiv Detail & Related papers (2020-08-24T23:44:51Z) - Information Consumption and Social Response in a Segregated Environment:
the Case of Gab [74.5095691235917]
This work provides a characterization of the interaction patterns within Gab around the COVID-19 topic.
We find that there are no strong statistical differences in the social response to questionable and reliable content.
Our results provide insights toward the understanding of coordinated inauthentic behavior and on the early-warning of information operation.
arXiv Detail & Related papers (2020-06-03T11:34:25Z) - Echo Chambers on Social Media: A comparative analysis [64.2256216637683]
We introduce an operational definition of echo chambers and perform a massive comparative analysis on 1B pieces of contents produced by 1M users on four social media platforms.
We infer the leaning of users about controversial topics and reconstruct their interaction networks by analyzing different features.
We find support for the hypothesis that platforms implementing news feed algorithms like Facebook may elicit the emergence of echo-chambers.
arXiv Detail & Related papers (2020-04-20T20:00:27Z) - Quantifying the Vulnerabilities of the Online Public Square to Adversarial Manipulation Tactics [43.98568073610101]
We use a social media model to quantify the impacts of several adversarial manipulation tactics on the quality of content.
We find that the presence of influential accounts, a hallmark of social media, exacerbates the vulnerabilities of online communities to manipulation.
These insights suggest countermeasures that platforms could employ to increase the resilience of social media users to manipulation.
arXiv Detail & Related papers (2019-07-13T21:12:08Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.