Authors
Ziv Epstein, Farnaz Jahanbakhsh, Tiziano Piccardi, Axel Peytavin, Isabel Gallegos, Shardul Sapkota, Dora Zhao, Johan Ugander, Michael S Bernstein
Published in
Proceedings of the National Academy of Sciences of the United States of America. Volume 123. Issue 34. Pages e2610388123. Aug 25, 2026. Epub Aug 17, 2026.
Abstract
Social media feed algorithms rank content that is purported to be preferred by users, but the engagement behaviors that drive these algorithms are (at best) indirect proxies for users' explicitly self-stated values. Are the resulting feeds value aligned, and if not, why? We investigate this question by annotating the basic human values expressed in participants' X (Twitter) feeds (N = 715 US users), analyzing the relationship between the posts' value expressions and the posts' amplification in the ranked "For You" Page feed, and then comparing the amplified values to users' own values. We observe that the inventory of posts from followed accounts reflects users' self-stated values-but that there is an overall negative correlation (misalignment) between users' explicit values and the value expressions the algorithm is more likely to amplify. We turn to engagement behavior to understand this misalignment and observe that users' engagement behaviors can be misaligned with their stated values-likely causing the algorithm to learn and reflect these misaligned values. We also detect partisan differences consistent with this theory: While the algorithm amplifies values negatively correlated with both Democrats' and Republicans' self-stated values, they are more misaligned for Democrats. And in fact replying, a heavily weighted form of engagement, is associated with values that are less aligned for both Democrats' and Republicans' self-stated values, and is even more misaligned for Democrats. Taken together, these findings offer a glimpse into the tensions between the values that people hold and those that provoke reactions, and how these value tensions can produce misaligned outcomes.
PMID:
42607202
Bibliographic data and abstract were imported from PubMed on 18 Aug 2026.
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