A modularidade como instrumento preditivo do resultado de pleitos eleitorais: um estudo de caso
DOI:
https://doi.org/10.34117/bjdv9n10-101Keywords:
análise de redes complexas, medidas de centralidade, modularidade, resultado eleitoralAbstract
O uso das pesquisas de opinião para predição de tendências é uma prática utilizada desde o século XIX. As pesquisas eleitorais, como conhecemos hoje, tiveram seu início nos Estados Unidos da América (EUA) há mais de cem anos, sempre com o objetivo de prever, com uma margem de erro aceitável, o resultado de cada pleito. Entretanto, a realização de uma pesquisa implica em custos operacionais significativos, e.g. pessoal treinado, deslocamento, divulgação, software. Este artigo tem como objetivo explorar o uso de métricas de análise de redes, abordando as medidas de centralidade e, em particular, a modularidade, para a formulação de um modelo simples de predição de resultados eleitorais. O estudo tomou como base de dados mais de trezentos mil comentários coletados no Twitter às vésperas de três pleitos eleitorais, utilizados como estudos de caso. O uso da modularidade foi capaz de alcançar um erro absoluto médio de 1,59%, inferior às chamadas pesquisas de “boca de urna” que obtiveram, de forma combinada, 2,48%.
References
Bailar, B. (1987). Survey research in the united states. roots and emergence, 1890-1960. Science.
Bansal, B. and Srivastava, S. (2018). On predicting elections with hybrid topic based sentiment analysis of tweets. Procedia Computer Science, 135:346–353. The 3rd International Conference on Computer Science and Computational Intelligence (ICCSCI 2018) : Empowering Smart Technology in Digital Era for a Better Life.
Bermingham, A. and Smeaton, A. (2011). On using twitter to monitor political sentiment and predict election results. In Proceedings of the Workshop on Sentiment Analysis where AI meets Psychology (SAAIP 2011), pages 2–10, Chiang Mai, Thailand. Asian Federation of Natural Language Processing.
Beveridge, A. and Shan, J. (2016). Network of thrones. Math Horizons.
Boccaletti, S., Latora, V., Moreno, Y., Chavez, M., and Hwang, D.-U. (2006). Complex networks: Structure and dynamics. Physics Reports, 424(4):175–308.
Bonacich, P. (1972). Factoring and weighting approaches to status scores and clique identification. J Math Sociol.
Bonacich, P. (2007). Some unique properties of eigenvector centrality. Soc Networks.
Bródka, P., Skibicki, K., Kazienko, P., and Musial, K. (2012). A degree centrality in multi-layered social network. CoRR, abs/1210.5184.
Budiharto, W. and Meiliana, M. (2018). Prediction and analysis of indonesia presidential election from twitter using sentiment analysis. Journal of Big Data, 5(1).
Campbell, W., Dagli, C., and Weinstein, C. (2013). Social network analysis with content and graphs. Lincoln Laboratory Journal, 20(1):62–81.
Carneiro, M. G. (2016). Redes complexas para classificação de dados via conformidade de padrão, caracterização de importância e otimização estrutural. PhD thesis, Instituto de Ciências Matemáticas e de Computação, Universidade de São Paulo, São Carlos.
Ceravolo, P. and Guerretti, S. (2013). Testing social network metrics for measuring electoral success in the italian municipal campaign of 2011. In 2013 International Conference on Cloud and Green Computing, pages 342–347. IEEE.
Chauhan, P., Sharma, N., and Sikka, G. (2020). The emergence of social media data and sentiment analysis in election prediction. Journal of Ambient Intelligence and Humanized Computing, 12(2):2601–2627.
Clauset, A., Newman, M. E. J., and Moore, C. (2004). Finding community structure in very large networks. Physical Review E, 70(6).
De Figueirêdo, B. C. B., Nakamura, F. G., and Nakamura, E. F. (2021). A group-based centrality for undirected multiplex networks: A case study of the brazilian car wash operation. IEEE Access, 9:81946–81956.
Dokoohaki, N., Zikou, F., Gillblad, D., and Matskin, M. (2015). Predicting swedish elections with twitter: A case for stochastic link structure analysis. In 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pages 1269–1276. IEEE.
Fortunato, S. (2010). Community detection in graphs. Physics Reports, 486(3-5):75–174.
Freeman, L. C. (1978). Centrality in social networks conceptual clarification. Soc Networks.
Gayo-Avello, D. (2013). A meta-analysis of state-of-the-art electoral prediction from twitter data. Social Science Computer Review, 31(6):649–679.
Hyndman, R. (2006). Another look at forecast accuracy metrics for intermittent demand. Foresight: The International Journal of Applied Forecasting, 4:43–46.
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”. Social Science Computer Review, 30(2):229–234.
Kassraie, P., Modirshanechi, A., and Aghajan, H. K. (2017). Election vote share prediction using a sentiment-based fusion of twitter data with google trends and online polls. In Proceedings of the 6th International Conference on Data Science, Technology and Applications, DATA 2017, page 363–370, Setubal, PRT. SCITEPRESS - Science and Technology Publications, Lda.
Khan, A., Zhang, H., Boudjellal, N., Ahmad, A., Shang, J., Dai, L., Hayat, B., and Uddin, M. I. (2021). Election prediction on twitter: A systematic mapping study. Complexity (New York, N.Y.), 2021:1–27.
Metaxas, P., Mustafaraj, E., and Gayo-Avello, D. (2011). Limits of electoral predictions using twitter. Proceedings of the International AAAI Conference on Web and Social Media, 5(1):490–493.
Newman, M. E. J. (2006). Modularity and community structure in networks. Proceedings of the National Academy of Sciences, 103(23):8577–8582.
Otte, E. and Rousseau, R. (2002). Social network analysis: A powerful strategy, also for the information sciences. J Inf Sci.
Pereira, A. K. S., Vita, Y. M., da Silva Felix, G., Gimaque, J. M. F., Damasceno, M. L. S., and de Figueirêdo, B. C. B. (2022). Usando redes complexas na identificação de empresas fraudulentas em licitações públicas. In Anais do X Workshop de Computação Aplicada em Governo Eletrônico, pages 13–24. SBC.
Pereira, Francisco Carlos de Lima, Proposal for an Algorithm for Positioning of Splitter Optical Power to Networks GPON. (2021). Brazilian Journal of Development, DOI:10.34117/bjdv7n4-687.
Sabuncu, I., Balci, M. A., and Akgüller, Ö. (2020). Prediction of usa november 2020 election results using multifactor twitter data analysis method.
Salunkhe, P. and Deshmukh, S. N. (2017). Twitter based election prediction and analysis. International Research Journal of Engineering and Technology (IRJET), 4(10):539– 544.
Sanders, E. and van den Bosch, A. (2020). Optimising twitter-based political election prediction with relevance and sentiment filters. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 6158–6165, Marseille, France. European Language Resources Association.
Tjong Kim Sang, E. and Bos, J. (2012). Predicting the 2011 dutch senate election results with twitter. In Proceedings of the Workshop on Semantic Analysis in Social Media, EACL ’12, pages 53–60, USA. Association for Computational Linguistics.
Truong, Q.-D., Dkaki, T., and Truong, Q.-B. (2016). Graph methods for social network analysis. In Nature of Computation and Communication, volume 168, pages 276–286, Cham. Springer International Publishing.
Tu, X., Jiang, G., Song, Y., and Zhang, X. (2018). Novel multiplex pagerank in multilayer networks. IEEE Access, 6:12530–12538.
Tumasjan, A., Sprenger, T., Sandner, P., and Welpe, I. (2010). Predicting elections with twitter: What 140 characters reveal about political sentiment. Proceedings of the International AAAI Conference on Web and Social Media, 4(1):178–185.
Yavari, A., Hassanpour, H., Rahimpour Cami, B., and Mahdavi, M. (2022). Election prediction based on sentiment analysis using twitter data. International Journal of Engineering, 35(2):372–379.
