Abstract

Despite widespread concerns about the risks of AI-generated content (AIGC) to the integrity of social media discourse, little is known about its scale and scope, the actors responsible for its dissemination online, and the user responses it elicits. In this work, we measure and characterize the prevalence, spreaders, and emotional reception of AI-generated political images. Analyzing a large-scale dataset from Twitter/\(\mathbb {X}$ \) related to the 2024 U.S. Presidential Election, we find that approximately 12% of shared images are detected as AI-generated, and around 10% of users are responsible for sharing 80% of AI-generated images. AIGC superspreaders—defined as the users who not only share a high volume of AI-generated images but also receive substantial engagement through retweets—are more likely to be \(\mathbb {X}$ \) Premium subscribers, have a right-leaning orientation, and exhibit automated …

Date

2025

Authors

Zhiyi Chen, Jinyi Ye, Beverlyn Tsai, Emilio Ferrara, Luca Luceri

Book

Proceedings of the 36th ACM Conference on Hypertext and Social Media (Best Student Paper Honorable Mention)

Pages

11-21