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

2025-02-16

AI Summary

This study presents the first systematic quantification of multimodal AI-generated content (AIGC) during the 2024 U.S. presidential election on X (formerly Twitter), addressing its prevalence, diffusion structure, and responsible actors. Leveraging large-scale real-world data, we integrate CLIP-based image detection, LLM-powered text detection, social network analysis, and user profiling. Our findings reveal: (1) 12% of election-related images and 1.4% of associated texts are AI-generated; (2) AIGC diffusion follows a pronounced long-tail distribution—just 3% of text and 10% of image disseminators account for 80% of corresponding modality-specific AIGC; (3) ideologically right-leaning, X Premium subscribers exhibiting automated behavioral patterns emerge as “superspreaders,” with modality-specific AIGC usage (e.g., disproportionate image over text generation); (4) AIGC constitutes a significantly higher proportion of profile media among AI-image disseminators. These results expose structural imbalances and systemic risks posed by multimodal AIGC in political communication.

Technology Category

• Humans and AI: Voting
• Machine Learning: Multimodal Learning
• Application Domains: Humanities & Computational Social Science

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Web Mining and Content Analysis: Content-based information diffusion
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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.

Problem

• Analyze AI-generated content prevalence
• Identify AIGC sharing patterns
• Characterize AIGC spreaders behavior

Innovation

• AI-generated content analysis
• Multimodal data investigation
• Social media spreader profiling