Original
Value misalignment in X’s feed algorithm is a reflection of value tensions in engagement
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.
中文
X信息流算法中的价值错位是参与度中价值张力的反映
摘要
社交媒体信息流算法会对据称用户偏好的内容进行排序，但驱动这些算法的参与行为充其量只是用户明确自我陈述价值观的间接代理。由此产生的信息流是否在价值上一致？若不一致，原因何在？我们通过标注参与者X（Twitter）信息流中表达的基本人类价值观（N = 715名美国用户），分析帖子中的价值表达与这些帖子在排序后的“For You”页面信息流中获得放大之间的关系，然后将被放大的价值观与用户自身价值观进行比较，来研究这一问题。我们观察到，来自关注账号的帖子集合反映了用户的自我陈述价值观——但用户的明确价值观与算法更可能放大的价值表达之间总体呈负相关（错位）。我们转向参与行为以理解这种错位，并观察到用户的参与行为可能与其陈述价值观不一致——这很可能导致算法学习并反映这些错位的价值观。我们还发现了与该理论一致的党派差异：尽管算法放大的价值观与民主党人和共和党人的自我陈述价值观均呈负相关，但对民主党人而言错位程度更高。事实上，回复——一种权重很高的参与形式——所关联的价值观对民主党人和共和党人的自我陈述价值观而言都更不匹配，且对民主党人甚至更加错位。综合来看，这些发现让我们得以一窥人们所持有的价值观与那些引发反应的价值观之间的张力，以及这些价值张力如何产生错位的结果。
关键词
价值错位、社交媒体算法、参与行为、X（Twitter）、党派差异、基本人类价值观、信息流推荐