Motivation, Attention, and Visual Platform Design: How Moral Contagions Spread on TikTok and Instagram in the 2024 United States Presidential Election

2026-02-02

AI Summary

This study investigates how TikTok and Instagram jointly shape the moralized discourse of political issues during the 2024 U.S. presidential election through their platform architectures, user demographics, and issue framing. Drawing on a dataset of 3.15 million posts, the analysis integrates the extended Moral Foundations Dictionary (eMFD), time-series supply–demand modeling, and semantic network mining to offer the first systematic comparison between the two platforms. Findings reveal that TikTok’s algorithm facilitates the viral diffusion of cross-cutting moral content, whereas Instagram enhances supply–demand alignment specifically around economic issues. Cryptocurrency discussions emerge as highly moralized across both platforms. Moreover, significant divergences in event-response patterns and information structures underscore a synergistic mechanism whereby algorithmic design and semantic network topology jointly modulate moral communication dynamics.

Technology Category

• Natural Language Processing: Discourse, Pragmatics & Argument Mining
• Application Domains: Humanities & Computational Social Science
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community

Application Category

• Social Networks and Social Media: Media and governance, opinion dynamics, filter bubbles, polarization
• Web Mining and Content Analysis: Sentiment analysis and opinion mining
• Security and Privacy: Data transparency and provenance

Abstract

Visual social media platforms have become primary venues for political discourse, yet we know little about how moralization operates differently across platforms and topics. Analyzing 2,027,595 TikToks and 1,126,972 Instagram posts during the 2024 US presidential election, we demonstrate that issues are not necessarily inherently moralized, but a product of audience demographics, platform architecture, and partisan framing. Using temporal supply-demand analysis and moral foundations scoring (eMFD), we examine the dynamics of key electoral issues. Three key findings emerge. First, moralization patterns diverge dramatically by platform: TikTok's algorithm enabled viral spread of moralized abortion and immigration content despite lower supply, while Instagram amplified economic discourse that aligned supply and demand. Second, traditionally"pragmatic"economic issues became moralized-cryptocurrency discourse invoked loyalty and authority foundations more strongly than any other topic, framing regulation as government overreach. Third, platforms responded to different events: TikTok surged after Harris's nomination across all topics (96% reduction in supply volatility), while Instagram spiked around cryptocurrency policy developments. Semantic network analysis reveals TikTok's circular topology enables cross-cutting exposure while Instagram's fragmented structure isolates Harris from economic discourse. These findings demonstrate that understanding political moralization requires examining platform-specific ecosystems where architecture, demographics, and content strategy interact to determine which issues get moralized and how moral content spreads.

Problem

• moralization
• social media platforms
• political discourse
• platform architecture
• audience demographics

Innovation

• moral foundations theory
• temporal supply-demand analysis
• platform architecture
• semantic network analysis
• visual social media