Prevalence, Sharing Patterns, and Spreaders of Multimodal AI-Generated Content on X during the 2024 U.S. Presidential Election

• URL: http://arxiv.org/abs/2502.11248v1
• Date: Sun, 16 Feb 2025 19:55:29 GMT
• Title: Prevalence, Sharing Patterns, and Spreaders of Multimodal AI-Generated Content on X during the 2024 U.S. Presidential Election
• Authors: Zhiyi Chen, Jinyi Ye, Emilio Ferrara, Luca Luceri,
• Abstract summary: Superspreaders of AIGC are more likely to be X Premium subscribers with a right-leaning orientation and exhibit automated behavior. This study serves as a very first step toward understanding the role generative AI plays in shaping online socio-political environments.
• Score: 8.03589806827457
• License:
• Abstract: While concerns about the risks of AI-generated content (AIGC) to the integrity of social media discussions have been raised, little is known about its scale and the actors responsible for its dissemination online. In this work, we identify and characterize the prevalence, sharing patterns, and spreaders of AIGC in different modalities, including images and texts. Analyzing a large-scale dataset from X related to the 2024 U.S. Presidential Election, we find that approximately 12% of images and 1.4% of texts are deemed AI-generated. Notably, roughly 3% of text spreaders and 10% of image spreaders account for 80% of the AI-generated content within their respective modalities. Superspreaders of AIGC are more likely to be X Premium subscribers with a right-leaning orientation and exhibit automated behavior. Additionally, AI image spreaders have a higher proportion of AI-generated content in their profiles compared to AI text spreaders. This study serves as a very first step toward understanding the role generative AI plays in shaping online socio-political environments and offers implications for platform governance.

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