ArticlePNAS nexus2024
Incentivizing news consumption on social media platforms using large language models and realistic bot accounts.
Article in PNAS nexus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Ad-based social media interventions increase belief accuracy and generate pro-social opinions among non-news readers.PloS one · 2026Article
- Political identity, algorithmic news exposure, and trust in digital media: the sequential mediating associations of motivated reasoning and misinformation susceptibility.Frontiers in psychology · 2026Article
- Reading news on social media boosts knowledge, discernment and trust.Nature human behaviour · 2025Article
- Following news on social media boosts knowledge, belief accuracy and trust.Nature human behaviour · 2025Article
- Independently testing prosocial interventions: Methods and recommendations from 31 researchers.Annals of the New York Academy of Sciences · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.