Difusão do comportamento de engajamento musical na plataforma Last.fm
Resumo
Processos de difusão de informação em redes sociais online são diretamente afetados pela forma como indivíduos se relacionam uns com os outros, além da forma como adotam comportamentos nessas plataformas. A compreensão da forma como indivíduos agem e propagam suas ações em redes sociais é de fundamental importância em diversos contextos, como política, economia e cultura. Este trabalho tem como objetivo caracterizar e modelar o engajamento de usuários a músicas considerando suas interações sociais, que definem uma rede, e seus hábitos musicais, que definem seu perfil. A aplicação da metodologia ao contexto de músicas da banda Linkin Park na plataforma Last.fm ajudam a esclarecer quais os papeis dos perfis dos usuários e das suas inter-relações na difusão do comportamento de engajamento musical.Referências
Al-Garadi, M. A., Varathan, K. D., Ravana, S. D., Ahmed, E., Mujtaba, G., Khan, M. U. S., and Khan, S. U. (2018). Analysis of online social network connections for identification of influential users: Survey and open research issues. ACM Comput. Surv., 51(1).
Aljanaki, A., Wiering, F., and Veltkamp, R. C. (2010). Mining user behavior in last.fm: Exploring patterns of music consumption and social interaction. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR).
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.
Aljanaki, A., Wiering, F., and Veltkamp, R. C. (2010). Mining user behavior in last.fm: Exploring patterns of music consumption and social interaction. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR).
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.
Publicado
20/07/2025
Como Citar
SANTOS, Matheus Henrique B. dos; COSTA, Rian Wagner; HOTT, Henrique M. C.; XAVIER, Carolina Ribeiro; VIEIRA, Vinícius da Fonseca.
Difusão do comportamento de engajamento musical na plataforma Last.fm. 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.
