Diffusion of musical engagement behavior on the Last.fm platform
Abstract
The information diffusion processes in online social networks are directly affected by how individuals interact with each other, as well as how they adopt behaviors on these platforms. Understanding how individuals act and propagate their actions in social networks is of fundamental importance in various contexts, such as politics, economics, and culture. This study aims to characterize and model user engagement with music by considering their social interactions, which define a network, and their musical habits, which define their profile. Applying the methodology to the context of Linkin Park’s musics on the Last.fm platform helps clarify the roles of user profiles and their interrelations in the diffusion of musical engagement behavior.References
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Allcott, H. and Gentzkow, M. (2017). Social media and fake news in the 2016 election. The Journal of Economic Perspectives, 31(2):211–235.
Aral, S. and Nicolaides, C. (2017). Exercise contagion in a global social network. Nature Communications, 8(1):14753.
Babul, S., Hristova, D., Lima, A., Lambiotte, R., and Beguerisse-Díaz, M. (2024). Link me baby one more time: Social music discovery on spotify.
Badawy, A., Ferrara, E., and Lerman, K. (2018). Analyzing the digital traces of political manipulation: The 2016 russian interference twitter campaign. In 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pages 258–265.
Brin, S. and Page, L. (1998). The anatomy of a large-scale hypertextual web search engine. Computer Networks and ISDN Systems, 30(1–7):107–117. Archived from the original on 2015-09-27.
Campos, G., Ribeiro, J., Vieira, V., and Xavier, C. (2023). Estudo do impacto da seleção de sementes baseada em centralidade e em informações de comunidades sobrepostas. In Anais do XII Brazilian Workshop on Social Network Analysis and Mining, pages 163–174, Porto Alegre, RS, Brasil. SBC.
Cencetti, G., Contreras, D. A., Mancastroppa, M., and Barrat, A. (2023). Distinguishing simple and complex contagion processes on networks. Phys. Rev. Lett., 130:247401.
Ferreira, C. H., Murai, F., Silva, A. P., Almeida, J. M., Trevisan, M., Vassio, L., Mellia, M., and Drago, I. (2021). On the dynamics of political discussions on instagram: A network perspective. Online Social Networks and Media, 25:100155.
Granovetter, M. S. (1978). Threshold models of collective behavior. The American Journal of Sociology, 83(6):1420–1443.
Henneberger, A. K., Mushonga, D. R., and Preston, A. M. (2021). Peer influence and adolescent substance use: A systematic review of dynamic social network research. Adolescent Research Review, 6(1):57–73.
Jha, A. K. and Verma, N. K. (2024). Social media platforms and user engagement: A multi-platform study on one-way firm sustainability communication. Information Systems Frontiers, 26(1):177–194.
Kempe, D., Kleinberg, J., and Éva Tardos (2003). Maximizing the spread of influence through a social network. In Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’03, pages 137–146. ACM.
Kovanen, L., Saramaki, J., and Kaski, K. (2010). Reciprocity of mobile phone calls. arXiv: Physics and Society.
Leskovec, J., Adamic, L. A., and Huberman, B. A. (2007). The dynamics of viral marketing. ACM Trans. Web, 1(1):5–es.
Ling, C., Jiang, J., Wang, J., Thai, M. T., Xue, L., Song, J., Qiu, M., and Zhao, L. (2023). Deep graph representation learning and optimization for influence maximization. In Proceedings of the 40th International Conference on Machine Learning, ICML’23. JMLR.org.
Mønsted, B., Sapieżyński, P., Ferrara, E., and Lehmann, S. (2017). Evidence of complex contagion of information in social media: An experiment using twitter bots. PLOS ONE, 12(9):1–12.
Newman, M. E. J. (2002). Spread of epidemic disease on networks. Phys. Rev. E, 66:016128.
Newman, M. E. J. (2003). The structure and function of complex networks. SIAM Review, 45(2):167–256.
Nguyen, J. (2018). Politics and the twitter revolution: A brief literature review and implications for future research. Social Networking, 7(4):243–251.
Pastor-Satorras, R. and Vespignani, A. (2001). Epidemic spreading in scale-free networks. Physical Review Letters, 86(14):3200–3203.
Rosati, D. P., Woolhouse, M. H., Bolker, B. M., and Earn, D. J. D. (2021). Modelling song popularity as a contagious process. Proceedings of the Royal Society A, 477(2249):20210457.
Seaman, C., McQuaid, R., and Pearson, M. (2017). Social networking in family businesses in a local economy. Local Economy, 32(5):451–466.
Smith, M. D., Telang, R., and Catalini, C. (2018). Social media engagement with movies: Analyzing user interactions and sentiment on twitter. Journal of Marketing Research.
Tasente, T. (2020). The brexit on the facebook pages of the european institutions. Technium Social Sciences Journal, 3(1):63–75.
Traag, V. A. (2016). Complex contagion of campaign donations. PLOS ONE, 11(4):1–20.
Wang, C., Strathman, A., Lizardo, O., Hachen, D., Toroczkai, Z., and Chawla, N. (2011). Weighted reciprocity in human communication networks.
Zhu, Y., Tang, J., Tang, X., and Chen, L. (2021). Analysis of influence contribution in social advertising. Proc. VLDB Endow., 15(2):348–360.
Published
2025-07-20
How to Cite
SANTOS, Matheus Henrique B. dos; COSTA, Rian Wagner; HOTT, Henrique M. C.; XAVIER, Carolina Ribeiro; VIEIRA, Vinícius da Fonseca.
Diffusion of musical engagement behavior on the Last.fm platform. In: BRAZILIAN WORKSHOP ON SOCIAL NETWORK ANALYSIS AND MINING (BRASNAM), 14. , 2025, Maceió/AL.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 200-213.
ISSN 2595-6094.
DOI: https://doi.org/10.5753/brasnam.2025.9073.
