Mapping Influence Clusters: A Network Analysis of TikTok Influencer Co-Followership Among University Students
DOI:
https://doi.org/10.30871/jaic.v9i6.10340Keywords:
TikTok, Social Network Analysis, Influencer Marketing, Co-Followership, University Students, Digital Influence, Community Detection, Social Media AlgorithmsAbstract
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.
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[1] M. B. Mutanga, O. Ureke, and T. Chani, “Social Media and the COVID-19: South African and Zimbabwean Netizens’ Response to a Pandemic,” Indonesian Journal of Information Systems, pp. 1–14, Aug. 2021, doi: 10.24002/ijis.v4i1.4338.
[2] A. Bhandari and S. Bimo, “Why’s Everyone on TikTok Now? The Algorithmized Self and the Future of Self-Making on Social Media,” Soc Media Soc, vol. 8, no. 1, Jan. 2022, doi: 10.1177/20563051221086241.
[3] H. Kang and C. Lou, “AI agency vs. human agency: understanding human–AI interactions on TikTok and their implications for user engagement,” Journal of Computer-Mediated Communication, vol. 27, no. 5, Aug. 2022, doi: 10.1093/jcmc/zmac014.
[4] S. H. Taylor and Y. A. Chen, “The lonely algorithm problem: the relationship between algorithmic personalization and social connectedness on TikTok,” Journal of Computer-Mediated Communication, vol. 29, no. 5, Aug. 2024, doi: 10.1093/jcmc/zmae017.
[5] J. Lasser and N. Poechhacker, “Designing social media content recommendation algorithms for societal good,” Ann N Y Acad Sci, vol. 1548, no. 1, pp. 20–28, Jun. 2025, doi: 10.1111/nyas.15359.
[6] E. Bakshy, S. Messing, and L. A. Adamic, “Exposure to ideologically diverse news and opinion on Facebook,” Science (1979), vol. 348, no. 6239, pp. 1130–1132, Jun. 2015, doi: 10.1126/science.aaa1160.
[7] B. Kitchens, S. L. Johnson, and P. Gray, “Understanding Echo Chambers and Filter Bubbles: The Impact of Social Media on Diversification and Partisan Shifts in News Consumption,” MIS Quarterly, vol. 44, no. 4, pp. 1619–1649, Dec. 2020, doi: 10.25300/MISQ/2020/16371.
[8] U. Reviglio, “Serendipity by Design? How to Turn from Diversity Exposure to Diversity Experience to Face Filter Bubbles in Social Media,” 2017, pp. 281–300. doi: 10.1007/978-3-319-70284-1_22.
[9] C. A. Hoffner and B. J. Bond, “Parasocial relationships, social media, & well-being,” Curr Opin Psychol, vol. 45, p. 101306, Jun. 2022, doi: 10.1016/j.copsyc.2022.101306.
[10] H. Metzler and D. Garcia, “Social Drivers and Algorithmic Mechanisms on Digital Media,” Perspectives on Psychological Science, vol. 19, no. 5, pp. 735–748, Sep. 2024, doi: 10.1177/17456916231185057.
[11] H. Astleitner and S. Schlick, “The social media use of college students: Exploring identity development, learning support, and parallel use,” Active Learning in Higher Education, vol. 26, no. 1, pp. 231–254, Mar. 2025, doi: 10.1177/14697874241233605.
[12] S. Barta, D. Belanche, A. Fernández, and M. Flavián, “Influencer marketing on TikTok: The effectiveness of humor and followers’ hedonic experience,” Journal of Retailing and Consumer Services, vol. 70, p. 103149, Jan. 2023, doi: 10.1016/j.jretconser.2022.103149.
[13] H. Metzler and D. Garcia, “Social Drivers and Algorithmic Mechanisms on Digital Media,” Perspectives on Psychological Science, vol. 19, no. 5, pp. 735–748, Sep. 2024, doi: 10.1177/17456916231185057.
[14] H. Astleitner and S. Schlick, “The social media use of college students: Exploring identity development, learning support, and parallel use,” Active Learning in Higher Education, vol. 26, no. 1, pp. 231–254, Mar. 2025, doi: 10.1177/14697874241233605.
[15] W. Tafesse and B. P. Wood, “Followers’ engagement with instagram influencers: The role of influencers’ content and engagement strategy,” Journal of Retailing and Consumer Services, vol. 58, p. 102303, Jan. 2021, doi: 10.1016/j.jretconser.2020.102303.
[16] S. H. Mrisha and S. Xixiang, “The power of influence: How social media influencers are shaping consumer decision making in the digital age,” Journal of Consumer Behaviour, vol. 23, no. 4, pp. 1844–1853, Jul. 2024, doi: 10.1002/cb.2308.
[17] Y. Joshi, W. M. Lim, K. Jagani, and S. Kumar, “Social media influencer marketing: foundations, trends, and ways forward,” Electronic Commerce Research, vol. 25, no. 2, pp. 1199–1253, Apr. 2025, doi: 10.1007/s10660-023-09719-z.
[18] S. H. Taylor and Y. A. Chen, “The lonely algorithm problem: the relationship between algorithmic personalization and social connectedness on TikTok,” Journal of Computer-Mediated Communication, vol. 29, no. 5, Aug. 2024, doi: 10.1093/jcmc/zmae017.
[19] M. A. Javed, M. S. Younis, S. Latif, J. Qadir, and A. Baig, “Community detection in networks: A multidisciplinary review,” Journal of Network and Computer Applications, vol. 108, pp. 87–111, Apr. 2018, doi: 10.1016/j.jnca.2018.02.011.
[20] C. Alves de Castro, “Thematic analysis in social media influencers: who are they following and why?,” Front Commun (Lausanne), vol. 8, Sep. 2023, doi: 10.3389/fcomm.2023.1217684.
[21] Z. Yang, R. Algesheimer, and C. J. Tessone, “A Comparative Analysis of Community Detection Algorithms on Artificial Networks,” Sci Rep, vol. 6, no. 1, p. 30750, Aug. 2016, doi: 10.1038/srep30750.
[22] B. Pankratz, B. Kamiński, and P. Prałat, “Performance of community detection algorithms supported by node embeddings,” J Complex Netw, vol. 12, no. 4, Jun. 2024, doi: 10.1093/comnet/cnae035.
[23] P. Harrigan, T. M. Daly, K. Coussement, J. A. Lee, G. N. Soutar, and U. Evers, “Identifying influencers on social media,” Int J Inf Manage, vol. 56, p. 102246, Feb. 2021, doi: 10.1016/j.ijinfomgt.2020.102246.
[24] F. F. Leung, F. F. Gu, and R. W. Palmatier, “Online influencer marketing,” J Acad Mark Sci, vol. 50, no. 2, pp. 226–251, Mar. 2022, doi: 10.1007/s11747-021-00829-4.
[25] J. Lasser and N. Poechhacker, “Designing social media content recommendation algorithms for societal good,” Ann N Y Acad Sci, vol. 1548, no. 1, pp. 20–28, Jun. 2025, doi: 10.1111/nyas.15359.
[26] R. Al Mosharrafa, T. Akther, and F. K. Siddique, “Impact of social media usage on academic performance of university students: Mediating role of mental health under a cross‐sectional study in Bangladesh,” Health Sci Rep, vol. 7, no. 1, Jan. 2024, doi: 10.1002/hsr2.1788.
[27] E. K. Chowdhury, “Examining the benefits and drawbacks of social media usage on academic performance: a study among university students in Bangladesh,” Journal of Research in Innovative Teaching & Learning, Feb. 2024, doi: 10.1108/JRIT-07-2023-0097.
[28] X. Li and Q. Liu, “Social Media Use, eHealth Literacy, Disease Knowledge, and Preventive Behaviors in the COVID-19 Pandemic: Cross-Sectional Study on Chinese Netizens,” J Med Internet Res, vol. 22, no. 10, p. e19684, Oct. 2020, doi: 10.2196/19684.
[29] M. B. Mutanga and A. Abayomi, “Tweeting on COVID-19 pandemic in South Africa: LDA-based topic modelling approach,” African Journal of Science, Technology, Innovation and Development, vol. 14, no. 1, pp. 163–172, Jan. 2022, doi: 10.1080/20421338.2020.1817262.
[30] T. Chani, O. O. Olugbara, and B. Mutanga, “The Problem of Data Extraction in Social Media: A Theoretical Framework,” Journal of Information Systems and Informatics, vol. 5, no. 4, pp. 1363–1384, Dec. 2023, doi: 10.51519/journalisi.v5i4.585.
[31] T. M. Fagbola, A. Abayomi, M. B. Mutanga, and V. Jugoo, “Lexicon-Based Sentiment Analysis and Emotion Classification of Climate Change Related Tweets,” 2022, pp. 637–646. doi: 10.1007/978-3-030-96302-6_60.
[32] C. A. Hoffner and B. J. Bond, “Parasocial relationships, social media, & well-being,” Curr Opin Psychol, vol. 45, p. 101306, Jun. 2022, doi: 10.1016/j.copsyc.2022.101306.
[33] J. Liu and J.-S. Lee, “Social media influencers and followers’ loneliness: the mediating roles of parasocial relationship, sense of belonging, and social support,” Online Media and Global Communication, vol. 3, no. 4, pp. 607–630, Dec. 2024, doi: 10.1515/omgc-2024-0025.
[34] S. Lotun, V. M. Lamarche, A. Matran-Fernandez, and G. M. Sandstrom, “People perceive parasocial relationships to be effective at fulfilling emotional needs,” Sci Rep, vol. 14, no. 1, p. 8185, Apr. 2024, doi: 10.1038/s41598-024-58069-9.
[35] T. M. Fagbola, A. Abayomi, M. B. Mutanga, and V. Jugoo, “Lexicon-Based Sentiment Analysis and Emotion Classification of Climate Change Related Tweets,” 2022, pp. 637–646. doi: 10.1007/978-3-030-96302-6_60.
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Copyright (c) 2025 Murimo Mutanga, Muhammad Aizri Fadillah, Tarirai Chani , Sindiy Fortuna Anuardi
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