When"Likers''Go Private: Engagement With Reputationally Risky Content on X

2026-01-16

AI Summary

This study investigates whether users increase their engagement with high-reputation-risk content—such as posts from political or partisan accounts—after X (formerly Twitter) made likes private. Leveraging the platform’s policy change as a natural experiment, the authors combine a difference-in-differences analysis of over 150,000 tweets with a within-subjects survey experiment involving 203 users to quantify, for the first time at scale in a real-world setting, the effect of like privacy on interactions with sensitive content. The findings reveal no significant aggregate increase in likes received by such content following the policy shift. Although participants self-reported a modestly higher willingness to like high-risk posts, this did not translate into a statistically significant change in actual collective behavior. The study provides empirical evidence on how social media privacy mechanisms modulate the dissemination of sensitive content.

Technology Category

• Machine Learning: Privacy
• Application Domains: Humanities & Computational Social Science
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

Application Category

• Security and Privacy: Large-scale security measurements
• Social Networks and Social Media: Media and governance, opinion dynamics, filter bubbles, polarization
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

In June 2024, X/Twitter changed likes'visibility from public to private, offering a rare, platform-level opportunity to study how the visibility of engagement signals affects users'behavior. Here, we investigate whether hiding liker identities increases the number of likes received by high-reputational-risk content, content for which public endorsement may carry high social or reputational costs due to its topic (e.g., politics) or the account context in which it appears (e.g., partisan accounts). To this end, we conduct two complementary studies: 1) a Difference-in-Differences analysis of engagement with 154,122 posts by 1068 accounts before and after the policy change. 2) a within-subject survey experiment with 203 X users on participants'self-reported willingness to like different kinds of content. We find no detectable platform-level increase in likes for high-reputational-risk content (Study 1). This finding is robust for both between-group comparison of high- versus low-reputational-risk accounts and within-group comparison across engagement types (i.e., likes vs. reposts). Additionally, while participants in the survey experiment report modest increases in willingness to like high-reputational-risk content under private versus public visibility, these increases do not lead to significant changes in the group-level average likelihood of liking posts (Study 2). Taken together, our results suggest that hiding likes produces a limited behavioral response at the platform level. This may be caused by a gap between user intention and behavior, or by engagement driven by a narrow set of high-usage or automated accounts.

Problem

• reputational risk
• engagement visibility
• social media behavior
• content endorsement
• platform policy

Innovation

• engagement visibility
• reputational risk
• social media behavior
• natural experiment
• privacy policy