Reduced Engagement With Affectively Polarized Social Media Content

Received 2024 Mar 9; Accepted 2025 Jan 10; Collection date 2025. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit... The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from... To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The generation and distribution of hyper-partisan content on social media has gained millions of exposure across platforms, often allowing malevolent actors to influence and disrupt democracies.

The spread of this content is facilitated by real users’ engaging with it on platforms. The current study tests the efficacy of an ‘inoculation’ intervention via six online survey-based experiments in the UK and US. Experiments 1–3 (total N = 3276) found that the inoculation significantly reduced self-reported engagement with polarising stimuli. However, Experiments 4–6 (total N = 1878) found no effects on participants’ self-produced written text discussing the topic. The implications of these findings are discussed in the context of the literature on polarisation and previous interventions to reduce engagement with disinformation. Subject terms: Human behaviour, Psychology

A series of experiments tested an inoculation intervention to reduce engagement with affectively polarized content on social media. The intervention successfully reduced self-reported sharing of polarizing content but did not affect how users wrote about polarized topics. Inoculating Against Affective Polarization: A Series of Experiments Examining the Effectiveness of Preemptive Interventions A team of researchers from the University of Bristol conducted a series of six experiments to investigate whether "inoculation theory," a communication framework used to preemptively counter misinformation, could be effectively applied to mitigate... Affective polarization, characterized by animosity and distrust towards opposing political groups, poses a significant threat to democratic discourse and societal cohesion. The researchers hypothesized that preemptively exposing individuals to information about the manipulative tactics often used to fuel affective polarization would reduce their susceptibility to such tactics and decrease their likelihood of engaging with and...

This research program was meticulously pre-registered, with detailed methodologies and analysis plans outlined in advance to ensure transparency and rigor. Ethical approvals were obtained from the University of Bristol’s Psychology Ethics Committee, and informed consent was secured from all participants. The first two experiments focused on the context of Brexit in Great Britain. Participants, recruited through YouGov, were exposed to either an inoculation video explaining common manipulation techniques used in affectively polarized content or a control video about the British political system. Subsequently, they were presented with synthetic news headlines related to Brexit, some employing derogatory, affectively polarized language and others using more neutral phrasing. The key outcome measures were participants’ self-reported likelihood of clicking on and sharing the headlines.

Experiment 2 refined the inoculation video and utilized a broader range of outcome measures. These initial studies provided promising results, suggesting that inoculation could indeed reduce engagement with affectively polarized content. Experiment 3 shifted the focus to the United States, examining affective polarization in the context of the abortion debate. Using a similar methodology as the previous experiments, participants were randomly assigned to an inoculation or control condition, and then presented with real-world tweets containing news links related to Roe v. Wade. The headlines were selected based on their level of affective polarization, and some were subtly modified to heighten the presence of affectively charged language.

This experiment further solidified the findings of the earlier studies, demonstrating the generalizability of the inoculation approach across different political contexts and highly divisive issues. Experiments 4, 5, and 6 sought to investigate the impact of inoculation on the language used by individuals when expressing their own views on politically charged topics. Specifically, Experiment 4 focused on the abortion debate in the US, asking participants to write short essays expressing their opinions. The researchers then analyzed the text using natural language processing techniques to quantify the level of affective polarization present in participants’ writing. Experiments 5 and 6 refined this methodology, prompting participants to respond to simulated social media posts that opposed their own stance on abortion. Experiment 6 was a direct replication of Experiment 4, with added pre-screening to ensure a balanced representation of pro-choice and pro-life participants.

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. Affective polarization–when individuals have positive feelings about members of their party and negative, harsh views about members of the opposing party–has become an ever-growing concern. In recent years, especially with the rise of social media, many have begun to take notice of affective polarization. The literature has shown that social media has the effect of creating echo chambers in which individuals are only exposed to content promoting their own views and biases. While scholars have explored the potential for media literacy programs to decrease affective polarization, we don’t yet know whether media literacy programs can reduce the spread of affective polarization on social media. This paper explores whether media literacy programs could decrease affective polarization on social media and draws the conclusion that implementing a program like this is not only difficult but will likely have adverse effects...

These findings suggest that addressing the nation’s polarization will require legal regulations. An online, open-access, peer-reviewed journal (ISSN: 2996-2218) for high school and undergraduate students, providing a permanent DOI for every published article. ©2023 American Journal of Student Research (ISSN:2996-2218). All Rights Reserved Roles Conceptualization, Data curation, Formal analysis, Methodology, Software, Visualization, Writing – original draft, Writing – review & editing Affiliation Copenhagen Center for Social Data Science, University of Copenhagen, Copenhagen, Denmark

Roles Conceptualization, Methodology, Software, Writing – original draft, Writing – review & editing Affiliation CS Department, IT University of Copenhagen, Copenhagen, Denmark Concerns about polarization and hate speech on social media are widespread. Affective polarization, i.e., hostility among partisans, is crucial in this regard as it links political disagreements to hostile language online. However, only a few methods are available to measure how affectively polarized an online debate is, and the existing approaches do not investigate jointly two defining features of affective polarization: hostility and social distance. To address this methodological gap, we propose a network-based measure of affective polarization that combines both aspects – which allows them to be studied independently.

We show that our measure accurately captures the relation between the level of disagreement and the hostility expressed towards others (affective component) and whom individuals choose to interact with or avoid (social distance component). Applying our measure to a large-scale Twitter data set on COVID-19, we find that affective polarization was low in February 2020 and increased to high levels as more users joined the Twitter discussion in...

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