Pesquisa Eleitoral em Redes Sociais: Inclusão da Análise de Novas Dimensões
Resumo
Este trabalho tem como principal objetivo utilizar dados públicos de redes sociais para realizar pesquisas (análises) eleitorais. Embora alguns trabalhos já tenham focado nessa tarefa, nenhum deles levou em consideração a identificação de usuários únicos aliada a fatores inerentes do ambiente virtual, tais como a análise de sentimentos das mensagens, a detecção de spammers e de conteúdo jornalístico. Os resultados alcançados mostram que análises mais elaboradas são capazes de melhorar os números alcançados pela metodologia empregada em outros trabalhos.
Palavras-chave:
Redes Sociais, Pesquisa Eleitoral, Análise de Sentimentos
Referências
Benevenuto, F., Magno, G., Rodrigues, T., and Almeida, V. (2010). Detecting spammers on twitter. In Collaboration, Electronic messaging, Anti-Abuse and Spam Conference (CEAS).
Bora, N. N. (2012). Summarizing public opinions in tweets. In Journal Proceedings of CICLing 2012, CICLing 2012.
Culnan, M. J., McHugh, P. J., and Zubillaga, J. I. (2010). How large u.s. companies can use twitter and other social media to gain business value. MIS Quarterly Executive, 9(4):243–259.
Eiben, A. E. and Smith, J. E. (2003). Introduction to Evolutionary Computing. Springer- Verlag.
Gayo-Avello, D. (2012). “I wanted to predict elections with twitter and all i got was this lousy paper” – A balanced survey on election prediction using twitter data. CoRR - http://arxiv.org/abs/1204.6441.
Gayo-Avello, D., Metaxas, P., and Mustafaraj, E. (2011). Limits of electoral predictions using twitter. In Int. Conf. on Weblogs and Social Media (ICWSM), pages 490–493.
Ghosh, S., Viswanath, B., Kooti, F., Sharma, N. K., Korlam, G., Benevenuto, F., Ganguly, N., and Gummadi, K. P. (2012). Understanding and combating link farming in the twitter social network. In Int. Conf. on World Wide Web, WWW ’12, pages 61–70.
Gomide, J., Veloso, A., Meira, Jr., W., Almeida, V., Benevenuto, F., Ferraz, F., and Teixeira, M. (2011). Dengue surveillance based on a computational model of spatiotemporal locality of twitter. In Int. Web Science Conference, WebSci ’11, pages 3:1– 3:8.
Gonçalves, P., Araújo, M., Benevenuto, F., and Cha, M. (2013). Comparing and combining sentiment analysis methods. In Conf. on Online Social Networks, COSN ’13, pages 27–38.
Hu, X., Tang, J., Gao, H., and Liu, H. (2013). Unsupervised sentiment analysis with emotional signals. In Int. Conf. on World Wide Web, WWW ’13, pages 607–618.
Jungherr, A., Jürgens, P., and Schoen, H. (2012). Why the pirate party won the german
election of 2009 or the trouble with predictions: A response to Tumasjan, A., Sprenger, T. O., Sander, P. G., & Welpe, I. M. “Predicting elections with twitter: What 140 characters reveal about political sentiment”. Soc. Sci. Comput. Rev., 30(2):229–234.
Lumezanu, C., Feamster, N., and Klein, H. (2012). #bias: Measuring the tweeting behavior of propagandists. In Int. Conf. on Weblogs and Social Media (ICWSM), pages 210–217.
McCord, M. and Chuah, M. (2011). Spam detection on twitter using traditional classifiers. In Int. Conf. on Autonomic and trusted computing, ATC’11, pages 175–186.
Mukherjee, S., Malu, A., A.R., B., and Bhattacharyya, P. (2012). Twisent: a multistage system for analyzing sentiment in twitter. In Int. Conf. on Information and knowledge management, CIKM ’12, pages 2531–2534.
Ribeiro Jr., S., Jr., Z., Meira Jr., W., and Pappa, G. L. (2012). Positive or negative? using blogs to assess vehicles features. ENIA - Brazilian Conf. on Intelligent System.
Tumasjan, A., Sprenger, T., Sandner, P., and Welpe, I. (2010). Predicting elections with twitter: What 140 characters reveal about political sentiment. In Int. Conf. on Weblogs and Social Media (ICWSM), pages 178–185.
Witten, I. H. and Frank, E. (2005). Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, 2nd edition.
Bora, N. N. (2012). Summarizing public opinions in tweets. In Journal Proceedings of CICLing 2012, CICLing 2012.
Culnan, M. J., McHugh, P. J., and Zubillaga, J. I. (2010). How large u.s. companies can use twitter and other social media to gain business value. MIS Quarterly Executive, 9(4):243–259.
Eiben, A. E. and Smith, J. E. (2003). Introduction to Evolutionary Computing. Springer- Verlag.
Gayo-Avello, D. (2012). “I wanted to predict elections with twitter and all i got was this lousy paper” – A balanced survey on election prediction using twitter data. CoRR - http://arxiv.org/abs/1204.6441.
Gayo-Avello, D., Metaxas, P., and Mustafaraj, E. (2011). Limits of electoral predictions using twitter. In Int. Conf. on Weblogs and Social Media (ICWSM), pages 490–493.
Ghosh, S., Viswanath, B., Kooti, F., Sharma, N. K., Korlam, G., Benevenuto, F., Ganguly, N., and Gummadi, K. P. (2012). Understanding and combating link farming in the twitter social network. In Int. Conf. on World Wide Web, WWW ’12, pages 61–70.
Gomide, J., Veloso, A., Meira, Jr., W., Almeida, V., Benevenuto, F., Ferraz, F., and Teixeira, M. (2011). Dengue surveillance based on a computational model of spatiotemporal locality of twitter. In Int. Web Science Conference, WebSci ’11, pages 3:1– 3:8.
Gonçalves, P., Araújo, M., Benevenuto, F., and Cha, M. (2013). Comparing and combining sentiment analysis methods. In Conf. on Online Social Networks, COSN ’13, pages 27–38.
Hu, X., Tang, J., Gao, H., and Liu, H. (2013). Unsupervised sentiment analysis with emotional signals. In Int. Conf. on World Wide Web, WWW ’13, pages 607–618.
Jungherr, A., Jürgens, P., and Schoen, H. (2012). Why the pirate party won the german
election of 2009 or the trouble with predictions: A response to Tumasjan, A., Sprenger, T. O., Sander, P. G., & Welpe, I. M. “Predicting elections with twitter: What 140 characters reveal about political sentiment”. Soc. Sci. Comput. Rev., 30(2):229–234.
Lumezanu, C., Feamster, N., and Klein, H. (2012). #bias: Measuring the tweeting behavior of propagandists. In Int. Conf. on Weblogs and Social Media (ICWSM), pages 210–217.
McCord, M. and Chuah, M. (2011). Spam detection on twitter using traditional classifiers. In Int. Conf. on Autonomic and trusted computing, ATC’11, pages 175–186.
Mukherjee, S., Malu, A., A.R., B., and Bhattacharyya, P. (2012). Twisent: a multistage system for analyzing sentiment in twitter. In Int. Conf. on Information and knowledge management, CIKM ’12, pages 2531–2534.
Ribeiro Jr., S., Jr., Z., Meira Jr., W., and Pappa, G. L. (2012). Positive or negative? using blogs to assess vehicles features. ENIA - Brazilian Conf. on Intelligent System.
Tumasjan, A., Sprenger, T., Sandner, P., and Welpe, I. (2010). Predicting elections with twitter: What 140 characters reveal about political sentiment. In Int. Conf. on Weblogs and Social Media (ICWSM), pages 178–185.
Witten, I. H. and Frank, E. (2005). Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, 2nd edition.
Publicado
01/08/2014
Como Citar
MIRANDA FILHO, Renato; ALMEIDA, Jussara M.; PAPPA, Gisele L..
Pesquisa Eleitoral em Redes Sociais: Inclusão da Análise de Novas Dimensões. In: BRAZILIAN WORKSHOP ON SOCIAL NETWORK ANALYSIS AND MINING (BRASNAM), 3. , 2014, Brasília.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2014
.
p. 164-175.
ISSN 2595-6094.
