Abstract
This paper presents a new approach to quantify temporal novelties in Social Networks and, as a consequence, to identify changing points driven by the occurrence of new real-world events that influence the public opinion. Our approach starts using Text Mining tools to highlight the main key terms, that will be later used to create a temporal graph, thus preserving their relation into the original texts and their temporal dependencies. We also defined a new measure to quantify the way users’ opinions have been evolving over time. Finally, we propose a straightforward Concept Drift method to identify when the changing points happen. Our full approach was evaluated on a historical event in Brazil: the 2018 presidential election race. We have chosen this period due to the volume of publications that, definitely, stated Social Networks as the main mechanism for new political activism. Our good results emphasize the importance of our approach and open new possibilities to identify bots developed to just spread, for example, fake news.
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Notes
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By using the matrix notation, the weight can also be represented as \(m_{u,v}\).
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In this work,“i” and “j” are just iteration variables used in different contexts.
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More details about these preprocessing activities are provided in Sect. 5.2.
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We are using tweets in Portuguese due to the focus of our research, however the reader can follow the same preprocessing steps regardless the language of his/her texts.
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References
Borondo, J., Morales, A.J., Losada, J.C., Benito, R.M.: Characterizing and modeling an electoral campaign in the context of Twitter: 2011 Spanish presidential election as a case study. Chaos: Interdisc. J. Nonlinear Sci. 22(2), 023138 (2012). https://doi.org/10.1063/1.4729139
Borondo, J., Morales, A.J., Losada, J.C., Benito, R.M.: Analyzing the usage of social media during Spanish presidential electoral campaigns, pp. 785–792. IEEE, August 2016. https://doi.org/10.1109/ASONAM.2016.7752327
Box, G., Jenkins, G., Reinsel, G., Ljung, G.: Time Series Analysis: Forecasting and Control. Wiley Series in Probability and Statistics. Wiley, Hoboken (2015)
Caldarelli, G., et al.: A multi-level geographical study of Italian political elections from Twitter data. PLoS ONE 9(5), 1–11 (2014). https://doi.org/10.1371/journal.pone.0095809
Cormen, T., Leiserson, C., Rivest, R., Stein, C.: Introduction to Algorithms. Computer Science. MIT Press, Cambridge (2009)
Katragadda, S., Virani, S., Benton, R., Raghavan, V.: Detection of event onset using Twitter, pp. 1539–1546. IEEE, July 2016. https://doi.org/10.1109/IJCNN.2016.7727381
Lovins, J.B.: Development of a stemming algorithm. Mech. Transl. Comput. Linguist. 11(1–2), 22–31 (1968)
Luhn, H.P.: Key word-in-context index for technical literature (KWIC index). Am. Doc. 11(4), 288–295 (1960). https://doi.org/10.1002/asi.5090110403
de Mello, R.F., Rios, R.A., Pagliosa, P.A., Lopes, C.S.: Concept drift detection on social network data using cross-recurrence quantification analysis. Chaos: Interdisc. J. Nonlinear Sci. 28(8), 085719 (2018). https://doi.org/10.1063/1.5024241
de Mello, R.F., Vaz, Y., Ferreira, C.H.G., Bifet, A.: On learning guarantees to unsupervised concept drift detection on data streams. Expert Syst. Appl. 117, 90–102 (2019). https://doi.org/10.1016/j.eswa.2018.08.054
Nicosia, V., Tang, J., Mascolo, C., Musolesi, M., Russo, G., Latora, V.: Graph metrics for temporal networks. In: Holme, P., Saramäki, J. (eds.) Temporal Networks. UCS, pp. 15–40. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36461-7_2
Nunes, M., Vieira, F., Zavaglia, C., Sossolote, C., Hernandez, J.: The design of a Lexicon for Brazilian Portuguese: lessons learned and perspectives. In: The Proceedings of the II Workshop on Computational Processing of Written and Spoken Portuguese, pp. 61–70 (1996)
Pan, R.K., Saramäki, J.: Path lengths, correlations, and centrality in temporal networks. Phys. Rev. E 84(1), 016105 (2011)
Rios, R.A., Lopes, C.S., Sikansi, F.H.G., Pagliosa, P.A., de Mello, R.F.: Analyzing the public opinion on the Brazilian political and corruption issues. In: 2017 Brazilian Conference on Intelligent Systems (BRACIS), pp. 13–18, October 2017. https://doi.org/10.1109/BRACIS.2017.37
Rios, R.A., Pagliosa, P.A., Ishii, R.P., de Mello, R.F.: TSViz: a data stream architecture to online collect, analyze, and visualize tweets. In: Proceedings of the Symposium on Applied Computing (SAC 2017), pp. 1031–1036. ACM, New York (2017). https://doi.org/10.1145/3019612.3019811
Schlimmer, J.C., Granger Jr., R.H.: Incremental learning from noisy data. Mach. Learn. 1(3), 317–354 (1986). https://doi.org/10.1023/A:1022810614389
Silva, T.C., Zhao, L.: Machine Learning in Complex Networks, vol. 2016. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-17290-3
Smith, A., Anderson, M.: Social media use in 2018. Technical report, Pew Research Center, Washington, D.C., March 2018
Webster, J.J., Kit, C.: Tokenization as the initial phase in NLP. In: Proceedings of the 14th Conference on Computational Linguistics (COLING 1992), vol. 4, pp. 1106–1110. Association for Computational Linguistics, Stroudsburg (1992). https://doi.org/10.3115/992424.992434
Wilson, R.: Introduction to Graph Theory. Longman (1996)
Acknowledgment
This work was supported by CAPES (Coordination for the Improvement of Higher Education Personnel – Brazilian federal government agency), FAPESP (São Paulo Research Foundation) under the grant number 2013/07375-0 and INCT-DD (Instituto Nacional de Ciência & Tecnologia em Democracia Digital). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of CAPES, FAPESP, and INCT-DD.
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dos Santos, V.M.G., de Mello, R.F., Nogueira, T., Rios, R.A. (2020). Quantifying Temporal Novelty in Social Networks Using Time-Varying Graphs and Concept Drift Detection. In: Cerri, R., Prati, R.C. (eds) Intelligent Systems. BRACIS 2020. Lecture Notes in Computer Science(), vol 12320. Springer, Cham. https://doi.org/10.1007/978-3-030-61380-8_44
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