Detecção de eventos no Twitter através de Grafos de visibilidade natural
Resumo
A Internet vem nos fornecendo cada vez mais dados e informações, ajudando a compreender melhor os seus usuários e o ambiente que os rodeiam. Uma das abordagens usadas para detectar e compreender eventos que ocorrem ao redor do mundo vem sendo a análise de redes sociais, como o caso do Twitter, usado no presente artigo. Assim, considerando a mudança da dinâmica do comportamento dos dados após a presença de um evento, propomos um novo método de detecção baseado no cálculo de métricas de redes complexas aplicadas aos bigram extraídos do conteúdo de Tweets, identificando eventos por meios de mudanças de dinâmica do sistema. Para validar nossa proposta usamos dois conjuntos de dados coletados por [Aiello-2013], no qual observamos resultados satisfatórios quando comparados com as técnicas já presentes na literatura.
Palavras-chave:
Redes Social, Analise de Dados
Referências
Aiello, L. M., Petkos, G., Martin, C., Corney, D., Papadopoulos, S., Skraba, R., Göker, A., Kompatsiaris, I., and Jaimes, A. (2013). Sensing trending topics in twitter. IEEE Transactions on Multimedia, 15(6):1268-1282.
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Dou, W., Wang, X., Ribarsky, W., and Zhou, M. (2012). Event detection in social media data. In IEEE VisWeek Workshop on Interactive Visual Text Analytics-Task Driven Analytics of Social Media Content, pages 971-980.
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Humphries, M. D. and Gurney, K. (2008). Network 'small-world-ness': a quantitative method for determining canonical network equivalence. PloS one, 3(4):e0002051.
Jarvis, R. A. and Patrick, E. A. (1973). Clustering using a similarity measure based on shared near neighbors. IEEE Trans. Comput., 22(11):1025-1034.
Katti, S. and Rao, A. V. (1968). Handbook of the poisson distribution.
Lacasa, L., Luque, B., Ballesteros, F., Luque, J., and Nuno, J. C. (2008). From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences, 105(13):4972-4975.
Li, C., Sun, A., and Datta, A. (2012). Twevent: Segment-based event detection from tweets. In Proceedings of the 21st ACM International Conference on Information and Knowledge Management, CIKM '12, pages 155-164. ACM.
Mathioudakis, M. and Koudas, N. (2010). Twittermonitor: Trend detection over the twit-ter stream. In Proceedings of the 2010 ACM SIGMOD International Conference on Management of Data, SIGMOD '10, pages 1155-1158, New York, NY, USA. ACM.
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Murphy, K. P. and Russell, S. (2002). Dynamic bayesian networks: representation, infe-rence and learning.
Nguyen, D. T. and Jung, J. E. (2017). Real-time event detection for online behavioral analysis of big social data. Future Generation Computer Systems, 66:137 -145.
Nummiaro, K., Koller-Meier, E., and Van Gool, L. (2003). An adaptive color-based particle filter. Image and vision computing, 21(1):99-110.
Parikh, R. and Karlapalem, K. (2013). Et: Events from tweets. In Proceedings of the 22Nd International Conference on World Wide Web, WWW '13 Companion, pages 613-620, New York, NY, USA. ACM.
Petrović, 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.
Poisson, S. D. (1837). Probabilité des jugements en matière criminelle et en matière civile, précédées des règles générales du calcul des probabilitiés. Paris, France: Bachelier, 1:1837.
Rosso, O. A., Craig, H., and Moscato, P. (2009). Shakespeare and other english renais-sance authors as characterized by information theory complexity quantifiers. Physica A: Statistical Mechanics and its Applications, 388(6):916 -926.
Sakaki, T., Okazaki, M., and Matsuo, Y. (2010). Earthquake shakes twitter users: Real-time event detection by social sensors. In Proceedings of the 19th International Con-ference on World Wide Web, WWW '10, pages 851-860, New York, NY, USA. ACM.
Telesford, Q. K., Joyce, K. E., Hayasaka, S., Burdette, J. H., and Laurienti, P. J. (2011). The ubiquity of small-world networks. Brain connectivity, 1(5):367-375.
Van Dongen, S. M. (2000). Graph clustering by flow simulation. PhD thesis.
Weng, J. and Lee, B.-S. (2011). Event detection in twitter. ICWSM, 11:401-408.
Barros, P., Cardoso, I., A.F. Loureiro, A., and Ramos, H. S. (2018). Event detection in social media through phase transition of bigram entropy. In IEEE Symposium on Computers and Communications (ISCC), Natal, Brazil.
Blei, D. M., Ng, A. Y., and Jordan, M. I. (2003). Latent dirichlet allocation. Journal of machine Learning research, 3(Jan):993-1022.
Cataldi, M., Di Caro, L., and Schifanella, C. (2010). Emerging topic detection on twitter based on temporal and social terms evaluation. In Proceedings of the Tenth Interna-tional Workshop on Multimedia Data Mining, MDMKDD '10, pages 4:1-4:10, New York, NY, USA. ACM.
Choi, H.-J. and Park, C. H. (2019). Emerging topic detection in twitter stream based on high utility pattern mining. Expert Systems with Applications, 115:27 -36.
Dang, Q., Gao, F., and Zhou, Y. (2016). Early detection method for emerging topics based on dynamic bayesian networks in micro-blogging networks. Expert Syst. Appl., 57(C):285-295.
Deerwester, S., Duais, S., Furnas, G., Landauer, T., and Harshman, R. (1990). Indexing by latent semantics analysis. Journal of the American Society for Information Science, 41(6):391-407.
Dou, W., Wang, X., Ribarsky, W., and Zhou, M. (2012). Event detection in social media data. In IEEE VisWeek Workshop on Interactive Visual Text Analytics-Task Driven Analytics of Social Media Content, pages 971-980.
Ester, M., Kriegel, H.-P., Sander, J., and Xu, X. (1996). A density-based algorithm for discovering clusters a density-based algorithm for discovering clusters in large spa-tial databases with noise. In Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, KDD'96, pages 226-231. AAAI Press.
Evensen, G. (2003). The ensemble kalman filter: Theoretical formulation and practical implementation. Ocean dynamics, 53(4):343-367.
Gama, J.,Žliobaitė, I., Bifet, A., Pechenizkiy, M., and Bouchachia, A. (2014). A survey on concept drift adaptation. ACM computing surveys (CSUR), 46(4):44.
Humphries, M. D. and Gurney, K. (2008). Network 'small-world-ness': a quantitative method for determining canonical network equivalence. PloS one, 3(4):e0002051.
Jarvis, R. A. and Patrick, E. A. (1973). Clustering using a similarity measure based on shared near neighbors. IEEE Trans. Comput., 22(11):1025-1034.
Katti, S. and Rao, A. V. (1968). Handbook of the poisson distribution.
Lacasa, L., Luque, B., Ballesteros, F., Luque, J., and Nuno, J. C. (2008). From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences, 105(13):4972-4975.
Li, C., Sun, A., and Datta, A. (2012). Twevent: Segment-based event detection from tweets. In Proceedings of the 21st ACM International Conference on Information and Knowledge Management, CIKM '12, pages 155-164. ACM.
Mathioudakis, M. and Koudas, N. (2010). Twittermonitor: Trend detection over the twit-ter stream. In Proceedings of the 2010 ACM SIGMOD International Conference on Management of Data, SIGMOD '10, pages 1155-1158, New York, NY, USA. ACM.
Mockus, J. (1975). On the bayes methods for seeking the extremal point. IFAC Procee-dings Volumes, 8(1, Part 1):428 -431. 6th IFAC World Congress (IFAC 1975) -Part 1: Theory, Boston/Cambridge, MA, USA, August 24-30, 1975.
Murphy, K. P. and Russell, S. (2002). Dynamic bayesian networks: representation, infe-rence and learning.
Nguyen, D. T. and Jung, J. E. (2017). Real-time event detection for online behavioral analysis of big social data. Future Generation Computer Systems, 66:137 -145.
Nummiaro, K., Koller-Meier, E., and Van Gool, L. (2003). An adaptive color-based particle filter. Image and vision computing, 21(1):99-110.
Parikh, R. and Karlapalem, K. (2013). Et: Events from tweets. In Proceedings of the 22Nd International Conference on World Wide Web, WWW '13 Companion, pages 613-620, New York, NY, USA. ACM.
Petrović, 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.
Poisson, S. D. (1837). Probabilité des jugements en matière criminelle et en matière civile, précédées des règles générales du calcul des probabilitiés. Paris, France: Bachelier, 1:1837.
Rosso, O. A., Craig, H., and Moscato, P. (2009). Shakespeare and other english renais-sance authors as characterized by information theory complexity quantifiers. Physica A: Statistical Mechanics and its Applications, 388(6):916 -926.
Sakaki, T., Okazaki, M., and Matsuo, Y. (2010). Earthquake shakes twitter users: Real-time event detection by social sensors. In Proceedings of the 19th International Con-ference on World Wide Web, WWW '10, pages 851-860, New York, NY, USA. ACM.
Telesford, Q. K., Joyce, K. E., Hayasaka, S., Burdette, J. H., and Laurienti, P. J. (2011). The ubiquity of small-world networks. Brain connectivity, 1(5):367-375.
Van Dongen, S. M. (2000). Graph clustering by flow simulation. PhD thesis.
Weng, J. and Lee, B.-S. (2011). Event detection in twitter. ICWSM, 11:401-408.
Publicado
10/09/2019
Como Citar
TENORIO, Fernanda Ferreira Albuquerque; CHAGAS, Eduarda ; BARROS, Pedro ; RAMOS, Heitor S..
Detecção de eventos no Twitter através de Grafos de visibilidade natural. In: WORKSHOP DE COMPUTAÇÃO URBANA (COURB), 3. , 2019, Gramado.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 181-193.
ISSN 2595-2706.
DOI: https://doi.org/10.5753/courb.2019.7477.
