Networks of Fear: TikTok’s Misinformation on Sex Trafficking

• In Event: Crime and Justice in Digital Media

Abstract

This study investigates the network structure and role of hashtags in spreading sex trafficking misinformation on TikTok using Social Network Analysis (SNA). While SNA is widely applied to Twitter, this research extends it to TikTok, a platform with unique algorithmic features and user behaviors. The study explores: (1) What is the network structure of sex trafficking hashtags on TikTok? (2) Which hashtags are frequently used in viral sex trafficking content? It hypothesizes that sex trafficking-related hashtags form a densely connected network with #humantraffickingawareness as the central node. Data was collected via TikTok using diverse account types to mitigate algorithmic biases and analyzed through SNA techniques. Measuring network size, density, and components revealed a fragmented structure with low density but tightly connected clusters. Hashtag degree, betweenness centrality, and eigenvector centrality metrics identified key hashtags, including #fyp and #humantraffickingawareness, as central nodes shaping the network’s dynamics. Findings suggest a sparse network with multiple subgroups, yet central hashtags influence engagement and content spread, potentially contributing to misinformation amplification. This research offers insights into TikTok’s hashtag-driven engagement patterns, emphasizing the role of algorithmic amplification in reinforcing specific clusters. It contributes to understanding the structural dynamics of hashtag-based interactions and misinformation dissemination on platforms like TikTok.

Author

• Sandali Vithanage, University of Toronto