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

TL;DR

The study investigates how platform-specific ecosystems shape moralization of political issues during the 2024 US election by comparing TikTok and Instagram across abortion, immigration, and economy. It employs large-scale data collection (over 3 million posts), temporal supply-demand analysis, and the extended Moral Foundations Dictionary (eMFD) with sentiment weighting within the MAD model framework to link motivation, attention, and design. Key findings show divergent platform-driven moralization: TikTok amplifies moralized abortion and immigration content despite lower supply, while Instagram emphasizes economy content with aligned supply and demand; cryptocurrency regulation is particularly moralized via loyalty and authority frames. The results demonstrate that platform architecture and framing strategies interact with audience demographics to determine which issues become moralized and how their messages propagate, with important implications for understanding political persuasion on visual platforms.

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.

Figures (4)

• Figure 1: Supply and demand on (a, c) TikTok and (b, d) Instagram from January to November 2024 smoothed over 7-day intervals. Supply is plotted as posts per day and demand as views per post per day.
• Figure 2: Platform differences in sentiment-weighted moral framing across topics and candidates. Heatmaps show average moral sentiment by foundation on TikTok (a) and Instagram (b), estimated using the extended Moral Foundations Dictionary (eMFD) with all word–foundation probabilities.
• Figure 3: Average percentage of moral language across all posts, by topic.
• Figure 4: Most common 1000 hashtag pairs on topically relevant (a) TikTok and (b) Instagram are visualized as networks where each node represents a hashtag and its size denotes the frequency of usage. Nodes are connected if the respective hashtags have been used together on at least one post. Hashtags categorized as economy, immigration, pro-choice, and pro-life correspond to blue, orange, green, and red nodes, respectively. Uncategorized hashtags are represented by white nodes.