Explorando a complementaridade entre estratégias para detecção de usuários influentes no Twitter
Resumo
Usuários influentes desempenham um importante papel na difusão de informação em mídias sociais, uma vez que podem ditar a publicidade par-a-par, impactando tarefas como validação de marca, propaganda, etc. Apesar do número crescente de trabalhos que visam identificar tais usuários, explorando diferentes características, decidir sobre qual a melhor estratégia para cada domínio é uma tarefa complexa. Neste trabalho realizamos um estudo quantitativo sobre algumas das principais estratégias para detecção de usuários influentes. Como principais contribuições, destacamos uma melhor compreensão sobre as estratégias selecionadas e uma nova e eficaz abordagem de meta-aprendizagem, baseada no PCA, capaz de combiná-las linearmente.
Referências
Bakshy, E., Hofman, J. M., Mason, W. A., and Watts, D. J. (2011). Everyone’s an influencer: Quantifying influence on twitter. In Proc. 4th ACM WSDM. ACM.
Bonacich, P. and Lloyd, P. (2001). Eigenvector-like measures of centrality for asymmetric relations. Social Networks, 23(3):191–201.
Cha, M., Haddadi, H., Benevenuto, F., and Gummadi, P. K. (2010). Measuring user influence in twitter: The million follower fallacy. ICWSM.
Davis, C. (1962). The norm of the schur product operation. Numerische Mathematik.
Fagin, R., Kumar, R., and Sivakumar, D. (2003). Comparing top k lists. In Proc. 14th ACM-SIAM 2003.
Galeotti, A. and Goyal, S. (2010). The law of the few. The American Economic Review.
Golub, G. H. and Loan, C. F. V. (1996). Matrix Computations. Johns Hopkins University Press.
Ilyas, M. U. and Radha, H. (2011). Identifying influential nodes in online social networks using principal component centrality. In Communications (ICC), IEEE International Conference. IEEE.
Kitsak, M., Gallos, L. K., Havlin, S., Liljeros, F., Muchnik, L., Stanley, H. E., and Makse, H. A. (2010). Identification of influential spreaders in complex networks. Nature Physics.
Lee, C., Kwak, H., Park, H., and Moon, S. (2010). Finding influentials based on the temporal order of information adoption in twitter. In Proceedings of the 19th International Conference on World Wide Web. ACM.
Li, Y.-M., Lin, C.-H., and Lai, C.-Y. (2010). Identifying influential reviewers for wordof- mouth marketing. Electronic Commerce Research and Applications, (4).
McCown, F. and Nelson, M. L. (2007). Agreeing to disagree: Search engines and their public interfaces. In Proc. 7th ACM/IEEE-CS JCDL 2007. ACM.
Page, L., Brin, S., Motwani, R., and Winograd, T. (1999). The pagerank citation ranking: Bringing order to the web. Technical report.
Pearson, K. (1901). On lines and planes of closest fit to systems of points in space. Philosophical Magazine.
Silva, A., Guimarães, S., Meira, Jr., W., and Zaki, M. (2013). Profilerank: Finding relevant content and influential users based on information diffusion. In Proceedings of the 7th Workshop on Social Network Mining and Analysis. ACM.
Subbian, K., Sharma, D., Wen, Z., and Srivastava, J. (2013). Finding influencers in networks using social capital. In Proc. of 2013 IEEE/ACM ASONAM.
Wu, S., Hofman, J. M., Mason, W. A., and Watts, D. J. (2011). Who says what to whom on twitter. In Proc. the 20th WWW 2011.
Bonacich, P. and Lloyd, P. (2001). Eigenvector-like measures of centrality for asymmetric relations. Social Networks, 23(3):191–201.
Cha, M., Haddadi, H., Benevenuto, F., and Gummadi, P. K. (2010). Measuring user influence in twitter: The million follower fallacy. ICWSM.
Davis, C. (1962). The norm of the schur product operation. Numerische Mathematik.
Fagin, R., Kumar, R., and Sivakumar, D. (2003). Comparing top k lists. In Proc. 14th ACM-SIAM 2003.
Galeotti, A. and Goyal, S. (2010). The law of the few. The American Economic Review.
Golub, G. H. and Loan, C. F. V. (1996). Matrix Computations. Johns Hopkins University Press.
Ilyas, M. U. and Radha, H. (2011). Identifying influential nodes in online social networks using principal component centrality. In Communications (ICC), IEEE International Conference. IEEE.
Kitsak, M., Gallos, L. K., Havlin, S., Liljeros, F., Muchnik, L., Stanley, H. E., and Makse, H. A. (2010). Identification of influential spreaders in complex networks. Nature Physics.
Lee, C., Kwak, H., Park, H., and Moon, S. (2010). Finding influentials based on the temporal order of information adoption in twitter. In Proceedings of the 19th International Conference on World Wide Web. ACM.
Li, Y.-M., Lin, C.-H., and Lai, C.-Y. (2010). Identifying influential reviewers for wordof- mouth marketing. Electronic Commerce Research and Applications, (4).
McCown, F. and Nelson, M. L. (2007). Agreeing to disagree: Search engines and their public interfaces. In Proc. 7th ACM/IEEE-CS JCDL 2007. ACM.
Page, L., Brin, S., Motwani, R., and Winograd, T. (1999). The pagerank citation ranking: Bringing order to the web. Technical report.
Pearson, K. (1901). On lines and planes of closest fit to systems of points in space. Philosophical Magazine.
Silva, A., Guimarães, S., Meira, Jr., W., and Zaki, M. (2013). Profilerank: Finding relevant content and influential users based on information diffusion. In Proceedings of the 7th Workshop on Social Network Mining and Analysis. ACM.
Subbian, K., Sharma, D., Wen, Z., and Srivastava, J. (2013). Finding influencers in networks using social capital. In Proc. of 2013 IEEE/ACM ASONAM.
Wu, S., Hofman, J. M., Mason, W. A., and Watts, D. J. (2011). Who says what to whom on twitter. In Proc. the 20th WWW 2011.
Publicado
20/07/2015
Como Citar
NEVES, Alan; VIEIRA, Ramon; MOURÃO, Fernando; ROCHA, Leonardo.
Explorando a complementaridade entre estratégias para detecção de usuários influentes no Twitter. In: CONCURSO DE TRABALHOS DE INICIAÇÃO CIENTÍFICA DA SBC (CTIC-SBC), 34. , 2015, Recife.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2015
.
p. 31-40.
