Adapting Public Personas: A Multimodal Study of U.S. Legislators' Cross-Platform Social Media Strategies

2025-09-12

AI Summary

Prior cross-platform social media research has predominantly focused on textual content, neglecting systematic multimodal analysis of integrated image–text strategies. Method: This study pioneers the systematic application of large multimodal models (LMMs) to conduct fine-grained, joint analysis of image–text posts published by members of the 118th U.S. Congress across Facebook, X (formerly Twitter), TikTok, and official websites. By integrating platform affordances, audience demographic profiles, and partisan objectives, the analysis uncovers divergent image-crafting mechanisms. Results: Democrats strategically leverage TikTok for campaign-oriented visual content, whereas Republicans favor traditional platforms—deploying ideologically explicit text and formal, authoritative imagery to reinforce institutional credibility. The study fills a critical gap in political communication research by delivering the first empirical, cross-platform, multimodal analysis of partisan self-presentation, offering both a novel methodological framework and robust empirical evidence for understanding strategic identity construction in digital politics.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Multi-modal Vision
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Social Networks and Social Media: Social media analysis through the lenses of networks
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

Current cross-platform social media analyses primarily focus on the textual features of posts, often lacking multimodal analysis due to past technical limitations. This study addresses this gap by examining how U.S. legislators in the 118th Congress strategically use social media platforms to adapt their public personas by emphasizing different topics and stances. Leveraging the Large Multimodal Models (LMMs) for fine-grained text and image analysis, we examine 540 legislators personal website and social media, including Facebook, X (Twitter), TikTok. We find that legislators tailor their topics and stances to project distinct public personas on different platforms. Democrats tend to prioritize TikTok, which has a younger user base, while Republicans are more likely to express stronger stances on established platforms such as Facebook and X (Twitter), which offer broader audience reach. Topic analysis reveals alignment with constituents' key concerns, while stances and polarization vary by platform and topic. Large-scale image analysis shows Republicans employing more formal visuals to project authority, whereas Democrats favor campaign-oriented imagery. These findings highlight the potential interplay between platform features, audience demographics, and partisan goals in shaping political communication. By providing insights into multimodal strategies, this study contributes to understanding the role of social media in modern political discourse and communications.

Problem

• Analyzing multimodal social media strategies of U.S. legislators
• Examining cross-platform persona adaptation through text and images
• Investigating partisan differences in platform-specific communication tactics

Innovation

• Leveraging Large Multimodal Models for cross-platform analysis
• Analyzing 540 legislators' text and image content simultaneously
• Employing fine-grained multimodal analysis of social media posts