Synthetic Politics: Prevalence, Spreaders, and Emotional Reception of AI-Generated Political Images on X
Journal:
arXiv
Published Date:
Feb 16, 2025
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/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 X Premium subscribers, have
a right-leaning orientation, and exhibit automated behavior. Their profiles
contain a higher proportion of AI-generated images than non-superspreaders, and
some engage in extreme levels of AIGC sharing. Moreover, superspreaders' AI
image tweets elicit more positive and less toxic responses than their non-AI
image tweets. This study serves as one of the first steps toward understanding
the role generative AI plays in shaping online socio-political environments and
offers implications for platform governance.
