ArticlePloS one2023
Social media feedback and extreme opinion expression.
Article in PloS one, 2023. 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.
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Who cites it
5 citing papers in PubMed.
- Exploring Attitudes to Lung Cancer Screening in England: An Inductive Content Analysis of Online Commentary Following Media Announcement of a National Lung Cancer Screening Programme.Health expectations : an international journal of public participation in health care and health policy · 2026Article
- Others Like Me: How Issue-Position Groups Distort the Function of Morality by Manufacturing Consensus.Annals of the New York Academy of Sciences · 2026Article
- How positive and negative feedback following real interactions changes subsequent sender ratings.Scientific reports · 2025Article
- Associations of social network use and social network addictive behaviors with self-esteem in adolescents: the EHDLA study.Frontiers in psychiatry · 2025Article
- Meniscus-Related Videos on TikTok Are Widely Viewed and Shared but the Educational Quality for Patients Is Poor.Arthroscopy, sports medicine, and rehabilitation · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
On popular social media platforms such as Twitter, Facebook, Instagram, or Tiktok, the quantitative feedback received by content producers is asymmetric: counts of positive reactions such as 'likes,' or 'retweets,' are easily observed but similar counts of negative reactions are not directly available. We study how this design feature of social media platforms affects the expression of extreme opinions. Using simulations of a learning model, we compare two feedback environments that differ in terms of the availability of negative reaction counts. We find that expressed opinions are generally more extreme when negative reaction counts are not available than when they are. We rely on analyses of Twitter data and several online experiments to provide empirical support for key model assumptions and test model predictions. Our findings suggest that a simple design change might limit, under certain conditions, the expression of extreme opinions on social media.
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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.