Detecção de Conteúdo Relevante e Usuários Influentes no Twitter
Resumo
Redes sociais têm desempenhado um papel cada vez mais fundamental como um meio para a disseminação de informações, ideias e influências entre os seus membros. O problema alvo deste artigo é determinar tanto usuários influentes quanto conteúdo relevante, ou seja, ordenar tanto grupos de usuários quanto o conteúdo por eles disseminado. Propomos uma nova técnica que se baseia em uma definição intuitiva e circular de relevância e influência. Essa técnica é descrita em detalhes, assim como a sua implementação eficiente. Ela foi validada utilizando duas bases de dados reais do Twitter para fins de recomendações. Os resultados obtidos mostram que a técnica proposta apresenta ganhos de 37% quando comparada a um método de filtragem colaborativa, ao mesmo tempo que tanto usuários influentes quanto conteúdo relevantes e mostram superiores qualitativamente.
Palavras-chave:
Detecção de Conteúdo, Twitter, Análise de Influência
Referências
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Resnick, P. and Varian, H. (1997). Recommender systems. Commun. ACM, 40(3):56–58.
Romero, D. M., Galuba, W., Asur, S., and Huberman, B. A. (2011). Influence and passivity in social media. In Proceedings of the 20th international conference companion on World wide web, pages 113–114, New York, NY, USA.
Silva, A., Valiati, H., Guimarães, S., and Meira Jr, W. (2011). From individual behavior to influence networks: A case study on twitter. In Proc. of the 17th Brazilian Symposium on Multimedia, Hypermedia and Web, Porto Alegre, RS, Brazil. SBC.
Tong, H., Faloutsos, C., and Pan, J.-Y. (2006). Fast random walk with restart and its applications. In Proceedings of the Sixth International Conference on Data Mining, pages 613–622, Washington, DC, USA. IEEE Computer Society.
Uysal, I. and Croft, W. B. (2011). User oriented tweet ranking: a filtering approach to microblogs. In Proc. of the 20th ACM international conference on Information and knowledge management, pages 2261–2264, NY, NY, USA. ACM.
Weng, J., Lim, E.-P., Jiang, J., and He, Q. (2010). Twitterrank: finding topic-sensitive influential twitterers. In Proceedings of the 3rd ACM international conference on Web search and data mining, pages 261–270. ACM.
Brin, S. and Page, L. (1998). The anatomy of a large-scale hypertextual web search engine. Comput. Netw. ISDN Syst., 30(1-7):107–117.
Cha, M., Haddadi, H., Benevenuto, F., and Gummadi, K. P. (2010). Measuring User Influence in Twitter: The Million Follower Fallacy. In In Proceedings of the 4th International AAAI Conference on Weblogs and Social Media, Washington DC, USA.
Franceschet, M. (2011). Pagerank: standing on the shoulders of giants. Commun. ACM, 54(6):92–101.
Frobenius, G. (1912). Über Matrizen aus nicht Negativen Elementen.
Hannon, J., McCarthy, K., and Smyth, B. (2011). Finding useful users on twitter: twittomender the followee recommender. In Proc. of the 33rd European conf. on Advances in information retrieval, pages 784–787, Berlin, Heidelberg. Springer-Verlag.
Iribarren, J. L. and Moro, E. (2007). Information diffusion epidemics in social networks. ArXiv e-prints. [link].
Katz, E. and Paul, F. (2005). Personal Influence: The Part Played by People in the Flow of Mass Communications. Transaction Publishers.
Kleinberg, J. M. (1998). Authoritative sources in a hyperlinked environment. In Proc. of the 9th annual ACM-SIAM symposium on Discrete algorithms, pages 668–677, Philadelphia, PA, USA. Society for Industrial and Applied Mathematics.
Koren, Y. and Bell, R. M. (2011). Advances in collaborative filtering. In Recommender Systems Handbook, pages 145–186.
Leavitt, A., Burchard, E., Fisher, D., and Gilbert, S. (2009). The Influentials: New Approaches for Analyzing Influence on Twitter. Webecology Project.
Mises, R. V. and Pollaczek-Geiringer, H. (1929). Praktische Verfahren der Gleichungsauflösung. Zamm-zeitschrift Fur Angewandte Mathematik Und Mechanik, 9:58–77.
Resnick, P. and Varian, H. (1997). Recommender systems. Commun. ACM, 40(3):56–58.
Romero, D. M., Galuba, W., Asur, S., and Huberman, B. A. (2011). Influence and passivity in social media. In Proceedings of the 20th international conference companion on World wide web, pages 113–114, New York, NY, USA.
Silva, A., Valiati, H., Guimarães, S., and Meira Jr, W. (2011). From individual behavior to influence networks: A case study on twitter. In Proc. of the 17th Brazilian Symposium on Multimedia, Hypermedia and Web, Porto Alegre, RS, Brazil. SBC.
Tong, H., Faloutsos, C., and Pan, J.-Y. (2006). Fast random walk with restart and its applications. In Proceedings of the Sixth International Conference on Data Mining, pages 613–622, Washington, DC, USA. IEEE Computer Society.
Uysal, I. and Croft, W. B. (2011). User oriented tweet ranking: a filtering approach to microblogs. In Proc. of the 20th ACM international conference on Information and knowledge management, pages 2261–2264, NY, NY, USA. ACM.
Weng, J., Lim, E.-P., Jiang, J., and He, Q. (2010). Twitterrank: finding topic-sensitive influential twitterers. In Proceedings of the 3rd ACM international conference on Web search and data mining, pages 261–270. ACM.
Publicado
17/07/2012
Como Citar
VALIATI, Hérico; SILVA, Arlei; GUIMARÃES, Sara; MEIRA JR., Wagner.
Detecção de Conteúdo Relevante e Usuários Influentes no Twitter. In: BRAZILIAN WORKSHOP ON SOCIAL NETWORK ANALYSIS AND MINING (BRASNAM), 1. , 2012, Curitiba.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2012
.
p. 93-104.
ISSN 2595-6094.
