Wojcieszak et al No Polarization from Partisan News IJPP Forthcoming

Wojcieszak, Magdalena; Leeuw, Sjifra de; Menchen-Trevino, Ericka; Lee, Seungsu; Huang-Isherwood, Ke M.; Weeks, Brian E.

doi:10.31219/osf.io/hqmuy

Cited by 3 publications

(1 citation statement)

References 43 publications

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“…Both the effect sizes we observe and the variance left unexplained in our models suggest that social media are most likely part of a constellation of influences driving Americans closer to their team and further away from the opposition. Similar to a recent study that failed to find a relationship between partisan news exposure and polarization (Wojcieszak et al, 2021), our study suggests that social media may be taking a larger share of the blame for group-based political competition and conflict than is supported by the evidence. Future research needs to further clarify the magnitude of the role played by social media in the process of social sorting, while more frankly acknowledging other factors like racial conflict and illiberal politicians (Mason, 2018).…”

Section: Discussionsupporting

confidence: 85%

The Group Roots of Social Media Politics: Social Sorting Predicts Perceptions of and Engagement in Politics on Social Media

Lane

Moxley

McLeod

2023

Communication Research

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Research on political partisans suggests that social media offer ideal playing fields for the group game of politics. This study considers how political and social identities interact to influence political communication on social media. Using an original two-wave survey of Americans fielded during the 2020 election period, we analyzed how social media users’ levels of social sorting—the alignment between racial, religious, ideological, and political identities—related to perceptions of and engagement in politics on social media. Results suggest that those with higher (vs. lower) levels of social sorting were more likely to perceive their social media environments as dominated by political content and conflict, and populated with politically interested and like-minded people. Auto-regressive panel models suggested that social sorting and political use of social media may be reciprocally related. Findings indicate social sorting may be a key concept for unearthing the group roots of politics on social media.

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Section: Discussionsupporting

confidence: 85%

The Group Roots of Social Media Politics: Social Sorting Predicts Perceptions of and Engagement in Politics on Social Media

Lane

Moxley

McLeod

2023

Communication Research

View full text Add to dashboard Cite

show abstract

Incentivizing news consumption on social media platforms using large language models and realistic bot accounts

Askari,

Chhabra,

von Hohenberg

et al. 2024

PNAS Nexus

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Polarization, misinformation, declining trust, and wavering support for democratic norms are pressing threats to the US Exposure to verified and balanced news may make citizens more resilient to these threats. This project examines how to enhance users’ exposure to and engagement with verified and ideologically balanced news in an ecologically valid setting. We rely on a 2-week long field experiment on 28,457 Twitter users. We created 28 bots utilizing GPT-2 that replied to users tweeting about sports, entertainment, or lifestyle with a contextual reply containing a URL to the topic-relevant section of a verified and ideologically balanced news organization and an encouragement to follow its Twitter account. To test differential effects by gender of the bots, the treated users were randomly assigned to receive responses by bots presented as female or male. We examine whether our intervention enhances the following of news media organizations, sharing and liking of news content (determined by our extensive list of news media outlets), tweeting about politics, and liking of political content (determined using our fine-tuned RoBERTa NLP transformer-based model). Although the treated users followed more news accounts and the users in the female bot treatment liked more news content than the control, these results were small in magnitude and confined to the already politically interested users, as indicated by their pretreatment tweeting about politics. In addition, the effects on liking and posting political content were uniformly null. These findings have implications for social media and news organizations and offer directions for pro-social computational interventions on platforms.

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Nudging recommendation algorithms increases news consumption and diversity on YouTube

Yu,

Haroon,

Menchen-Trevino

et al. 2024

PNAS Nexus

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Recommendation algorithms profoundly shape users’ attention and information consumption on social media platforms. This study introduces a computational intervention aimed at mitigating two key biases in algorithms by influencing the recommendation process. We tackle \interest bias, or algorithms creating narrow non-news and entertainment information diets, and ideological bias, or algorithms directing the more strongly partisan users to like-minded content. Employing a sock-puppet experiment (N = 8,600 sock puppets) alongside a month-long randomized experiment involving 2,142 frequent YouTube users, we investigate if nudging the algorithm by playing videos from verified and ideologically balanced news channels in the background increases recommendations to and consumption of news. We additionally test if providing balanced news input to the algorithm promotes diverse and cross-cutting news recommendations and consumption. We find that nudging the algorithm significantly and sustainably increases both recommendations to and consumption of news and also minimizes ideological biases in recommendations and consumption, particularly among conservative users. In fact, recommendations have stronger effects on users’ exposure than users’ exposure has on subsequent recommendations. In contrast, nudging the users has no observable effects on news consumption. Increased news consumption has no effects on a range of survey outcomes (e.g., knowledge, participation, polarization, misperceptions), adding to the growing evidence of limited attitudinal effects of on-platform exposure. The intervention does not adversely affect user engagement on YouTube, showcasing its potential for real-world implementation. These findings underscore the influence wielded by platform recommender algorithms on users’ attention and information exposure.

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Wojcieszak et al No Polarization from Partisan News IJPP Forthcoming

Cited by 3 publications

References 43 publications

The Group Roots of Social Media Politics: Social Sorting Predicts Perceptions of and Engagement in Politics on Social Media

The Group Roots of Social Media Politics: Social Sorting Predicts Perceptions of and Engagement in Politics on Social Media

Incentivizing news consumption on social media platforms using large language models and realistic bot accounts

Nudging recommendation algorithms increases news consumption and diversity on YouTube

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