Mapping Influence Clusters: A Network Analysis of TikTok Influencer Co-Followership Among University Students - Mutanga, Murimo; Fadillah, Muhammad Aizri; Chani , Tarirai; Anuardi, Sindiy Fortuna (Journal of Applied Informatics and Computing, 2025)
This study examines TikTok influencer co-followership patterns among university students through social network analysis to understand how shared influence functions within digital ecosystems. Using survey data from Indonesian university students who identified their top three most-followed TikTok influencers, we built a co-followership network comprising 266 unique influencers connected by 333 relationships. The research employed quantitative network analysis methods, such as centrality measures, community detection algorithms, and content categorisation, to map influence clusters and explore the network’s structural properties. Results reveal a fragmented network with a low density (0.0094) consisting of 49 connected components, indicating that student followership patterns form distinct thematic communities rather than a single, unified influence network. Centrality analysis identified key bridging influencers, with Tasya Farasya emerging as the most central figure, demonstrating broad appeal across multiple interest categories. Community detection uncovered clear clusters organised around lifestyle and entertainment content, comedy, food, educational material, and motivational themes. Content analysis revealed that travel and lifestyle influencers dominated the network (23.7%), followed by comedy and entertainment creators (16.9%), reflecting TikTok's dual role as both an entertainment platform and a lifestyle guide for university students. The findings show how algorithmic personalisation creates confined influence communities while some central figures act as bridges across different content domains. This research advances methodological approaches by pioneering network analysis methods for influencer co-followership, thereby enhancing the understanding of digital influence as a networked rather than individual phenomenon. The results provide valuable insights for marketing professionals aiming to understand network influence, educational institutions developing media literacy programmes, and platform designers creating algorithmic recommendation systems.