Uma Abordagem para Identificação de Entidades Influentes em Eventos Comentados nas Redes Sociais Online
Resumo
Redes Sociais Online (RSO) permitem aos usuários compartilhar conteúdo de qualquer tipo. Nestas redes, os usuários podem ser vistos como sensores sociais, onde suas opini˜oes e comentários a respeito de um evento podem ser utilizados para estudo (e.g., caracterização de eventos, reconhecimento de entidades influentes). RSO podem ser modeladas como redes complexas, onde as entidades são representadas pelos vértices e as arestas caracterizam a conexão entra elas. Utilizando esta abordagem, podemos determinar quais são as entidades mais influentes no contexto estudado através de medidas de centralidade (e.g., Betweenness e Pagerank). Com base nisto, este artigo apresenta uma análise das entidades mais fluentes sob a perspectiva de um evento em Redes Sociais Online e apresenta um estudo das comunidades detectadas. Como resultados percebemos que as medidas de centralidade apresentam diferentes entidades influentes, isto demonstra que embora todas as entidades façam parte do mesmo evento, algumas tem influência maior dependendo do aspecto estudado. Com relação a detecção de comunidades percebemos que entidades que foram agrupadas juntas, estão diretamente ligadas a um sub-evento dentro do contexto estudado.
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Brandes, U. and Erlebach, T. (2005). Network analysis: methodological foundations, volume 3418. Springer Science & Business Media.
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Kumar, R., Novak, J., and Tomkins, A. (2010). Structure and evolution of online social networks. In Link mining: models, algorithms, and applications, pages 337–357. Springer.
Liu, X. F., Liu, Y.-L., Lu, X.-H., Wang, Q.-X., and Wang, T.-X. (2016). The anatomy of the global football player transfer network: Club functionalities versus network properties. PloS One, 11(6):e0156504.
Mislove, A., Marcon, M., Gummadi, K. P., Druschel, P., and Bhattacharjee, B. (2007). Measurement and analysis of online social networks. In Proceedings of the 7th of Internet Measurement Conference, pages 29–42. ACM.
Newman, M. E. (2003). The structure and function of complex networks. SIAM review, 45(2):167–256.
Newman, M. E. (2006). Modularity and community structure in networks. Proceedings of the national academy of sciences, 103(23):8577–8582.
Newman, M. E. (2008). The mathematics of networks. The new palgrave encyclopedia of economics, 2(2008):1–12.
Oliveira, L. S., Amaral, M. S., and Aguiar, I. S. (2015). O parlamento brasileiro no twitter: uma análise de rede social. In Proceedings of the 12th Simpósio Brasileiro de Sistemas Colaborativos, pages 138–143.
Petrovíc, S., Osborne, M., and Lavrenko, V. (2010). Streaming first story detection with application to twitter. In Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, pages 181–189. Association for Computational Linguistics.
Rochat, Y. (2009). Closeness centrality extended to unconnected graphs: The harmonic centrality index. In Proceedings of the 6th International Conference on Applications of Social Network Analysis.
Rostami, A. and Mondani, H. (2015). The complexity of crime network data: A case study of its consequences for crime control and the study of networks. PloS one, 10(3):e0119309.
Sakaki, T., Okazaki, M., and Matsuo, Y. (2010). Earthquake shakes Twitter users: realtime event detection by social sensors. In Proceedings of the 19th International Conference on World Wide Web, pages 851–860. ACM.
Souza, B. A., Almeida, T. G., Menezes, A. A., Nakamura, F. G., Figueiredo, C. M., and Nakamura, E. F. (2016). For or against?: Polarity analysis in tweets about impeach-ment process of brazil president. In Proceedings of the 22Nd Brazilian Symposium on Multimedia and the Web, pages 335–338. ACM.
Almeida, T. G., Souza, B. A., Menezes, A. A., Figueiredo, C. M., and Nakamura, E. F. (2016). Sentiment analysis of Portuguese comments from Foursquare. In Proceedings of the 22Nd Brazilian Symposium on Multimedia and the Web, pages 355–358. ACM.
Atefeh, F. and Khreich, W. (2015). A survey of techniques for event detection in twitter. Computational Intelligence, 31(1):132–164.
Barrat, A., Barthelemy, M., Pastor-Satorras, R., and Vespignani, A. (2004). The architecture of complex weighted networks. Proceedings of the National Academy of Sciences of the United States of America, 101(11):3747–3752.
Benevenuto, F. R., Silveira, D., Bombonato, L., Fortes, R., and Pereira Junior, á . R. (2011). Entendendo a twitteresfera brasileira. In Proceedings of the 8th Simpósio Brasileiro de Sistemas Colaborativos.
Beveridge, A. and Shan, J. (2016). Network of thrones. Math Horizons, pages 18–22.
Blondel, V. D., Guillaume, J.-L., Lambiotte, R., and Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of statistical mechanics: theory and experiment, 2008(10):P1000Brandes, U. (2001). A faster algorithm for betweenness centrality. Journal of mathematical sociology, 25(2):163–177.
Brandes, U. and Erlebach, T. (2005). Network analysis: methodological foundations, volume 3418. Springer Science & Business Media.
Cha, M., Haddadi, H., Benevenuto, F., and Gummadi, P. K. (2010). Measuring user influence in twitter: The million follower fallacy. In Proceedings of the 4th International AAAI Conference On Web and Social Media, pages 335–338. AAAI.
Freeman, L. C. (1978). Centrality in social networks conceptual clarification. Social networks, 1(3):215–239.
Jamali, M. and Abolhassani, H. (2006). Different aspects of social network analysis. In Proceedings of the International Conference on Web Intelligence, pages 66–72. IEEE.
Kumar, R., Novak, J., and Tomkins, A. (2010). Structure and evolution of online social networks. In Link mining: models, algorithms, and applications, pages 337–357. Springer.
Liu, X. F., Liu, Y.-L., Lu, X.-H., Wang, Q.-X., and Wang, T.-X. (2016). The anatomy of the global football player transfer network: Club functionalities versus network properties. PloS One, 11(6):e0156504.
Mislove, A., Marcon, M., Gummadi, K. P., Druschel, P., and Bhattacharjee, B. (2007). Measurement and analysis of online social networks. In Proceedings of the 7th of Internet Measurement Conference, pages 29–42. ACM.
Newman, M. E. (2003). The structure and function of complex networks. SIAM review, 45(2):167–256.
Newman, M. E. (2006). Modularity and community structure in networks. Proceedings of the national academy of sciences, 103(23):8577–8582.
Newman, M. E. (2008). The mathematics of networks. The new palgrave encyclopedia of economics, 2(2008):1–12.
Oliveira, L. S., Amaral, M. S., and Aguiar, I. S. (2015). O parlamento brasileiro no twitter: uma análise de rede social. In Proceedings of the 12th Simpósio Brasileiro de Sistemas Colaborativos, pages 138–143.
Petrovíc, S., Osborne, M., and Lavrenko, V. (2010). Streaming first story detection with application to twitter. In Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, pages 181–189. Association for Computational Linguistics.
Rochat, Y. (2009). Closeness centrality extended to unconnected graphs: The harmonic centrality index. In Proceedings of the 6th International Conference on Applications of Social Network Analysis.
Rostami, A. and Mondani, H. (2015). The complexity of crime network data: A case study of its consequences for crime control and the study of networks. PloS one, 10(3):e0119309.
Sakaki, T., Okazaki, M., and Matsuo, Y. (2010). Earthquake shakes Twitter users: realtime event detection by social sensors. In Proceedings of the 19th International Conference on World Wide Web, pages 851–860. ACM.
Souza, B. A., Almeida, T. G., Menezes, A. A., Nakamura, F. G., Figueiredo, C. M., and Nakamura, E. F. (2016). For or against?: Polarity analysis in tweets about impeach-ment process of brazil president. In Proceedings of the 22Nd Brazilian Symposium on Multimedia and the Web, pages 335–338. ACM.
Publicado
02/07/2017
Como Citar
M. NETO, Rayol; SOUZA, Bruno Á.; ALMEIDA, Thais G.; NAKAMURA, Fabíola G.; NAKAMURA, Eduardo F..
Uma Abordagem para Identificação de Entidades Influentes em Eventos Comentados nas Redes Sociais Online. In: SIMPÓSIO BRASILEIRO DE SISTEMAS COLABORATIVOS (SBSC), 14. , 2017, São Paulo.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2017
.
p. 1314-1327.
ISSN 2326-2842.
DOI: https://doi.org/10.5753/sbsc.2017.9956.
