Abstract
Along with the popularization and rapid development of Internet, there is a growing interest in the research to identify the trend of social events on social media. Currently news could quickly spread on various social media (e.g. Sina Weibo) with a limited time, which may trigger the severity of the events that requires timely attention and responses from government. This paper proposes to predict the trend of social events on Sina Weibo, which is the most popular social media in China now. In this study, combining social psychology and communication sciences, we extracted comprehensive and effective features which may relate to the trend of social events on social media, and constructed the trend prediction models using three classical regression algorithms. The real social events data was used to verify the performance of our model, and the outstanding performance with precision of 0.56 and an f-measure of 0.71 demonstrate the efficiency of our features and models.
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References
Achrekar, H., Gandhe, A., Lazarus, R., Yu, S.-H., Liu, B.: Online social networks flu trend tracker: a novel sensory approach to predict flu trends. In: Gabriel, J., Schier, J., Van Huffel, S., Conchon, E., Correia, C., Fred, A., Gamboa, H. (eds.) BIOSTEC 2012. CCIS, vol. 357, pp. 353–368. Springer, Heidelberg (2013). doi:10.1007/978-3-642-38256-7_24
Agarwal, P.: Prediction of trends in online social netwok. Ph.D. thesis, Indian Institute of Technology New Delhi (2013)
Brunsting, S., Postmes, T.: Social movement participation in the digital age predicting offline and online collective action. Small Group Res. 33(5), 525–554 (2002)
De Mol, C., De Vito, E., Rosasco, L.: Elastic-net regularization in learning theory. J. Complex. 25(2), 201–230 (2009)
Ekman, P.: Facial expression and emotion. Am. Psychol. 48(4), 384 (1993)
Glasbergen, P.: Global action networks: agents for collective action. Glob. Environ. Change 20(1), 130–141 (2010)
Goio, F., Gurr, T.R.: Why Men Rebel. Princeton University Press, Princeton (1974)
Granovetter, M.: Threshold models of collective behavior. Am. J. Sociol. 83, 1420–1443 (1978)
Hans, C.: Bayesian lasso regression. Biometrika 96(4), 835–845 (2009)
Hoerl, A.E., Kennard, R.W.: Ridge regression: biased estimation for nonorthogonal prolems. Technometrics 12(1), 55–67 (1970)
Hornsey, M.J., Blackwood, L., Louis, W., Fielding, K., Mavor, K., Morton, T., O’Brien, A., Paasonen, K.E., Smith, J., White, K.M.: Why do people engage in collective action? Revisiting the role of perceived effectiveness. J. Appl. Soc. Psychol. 36(7), 1701–1722 (2006)
Ivancevich, J.M., Matteson, M.T., Konopaske, R.: Organizational Behavior and Management. Bpi/Irwin (1990)
Kaleel, S.B., Abhari, A.: Cluster-discovery of Twitter messages for event detection and trending. J. Comput. Sci. 6, 47–57 (2015)
Khan, S.: Mining news articles to predict a stock trend (2014)
Kumar, A., Naughton, J., Patel, J.M., Zhu, X.: To join or not to join? Thinking twice about joins before feature selection. In: Proceedings of the 2016 ACM SIGMOD International Conference on Management of Data, SIGMOD, vol. 16 (2016)
Kwak, H., Lee, C., Park, H., Moon, S.: What is Twitter, a social network or a news media? In: Proceedings of the 19th International Conference on World Wide Web. pp. 591–600. ACM (2010)
Tung, C., Lu, W.: Analyzing depression tendency of web posts using an event-driven depression tendency warning model. Artif. Intell. Med. 66, 53–62 (2016)
Van Zomeren, M., Postmes, T., Spears, R.: Toward an integrative social identity model of collective action: a quantitative research synthesis of three socio-psychological perspectives. Psychol. Bull. 134(4), 504 (2008)
Van Zomeren, M., Spears, R.: Metaphors of protest: a classification of motivations for collective action. J. Soc. Issues 65(4), 661–679 (2009)
Van Zomeren, M., Spears, R., Fischer, A.H., Leach, C.W.: Put your money where your mouth is! Explaining collective action tendencies through group-based anger and group efficacy. J. Pers. Soc. Psychol. 87(5), 649 (2004)
Wan, M., Liu, L., Qiu, J., Yang, X.: Collective action: definition, psychological mechanism and behavior measurement. Adv. Psychol. Sci. 19(5), 723–730 (2011)
Wright, S.C.: The next generation of collective action research. J. Soc. Issues 65(4), 859–879 (2009)
Wright, S.C., Taylor, D.M., Moghaddam, F.M.: Responding to membership in a disadvantaged group: from acceptance to collective protest. J. Personal. Soc. Psychol. 58(6), 994 (1990)
Yu, X.L.L.H.P., Jianmei, R.H.C.: Constructing the affective lexicon ontology. J. China Soc. Sci. Tech. Inf. 2, 006 (2008)
Zhao, L., Chen, F., Dai, J., Hua, T., Lu, C.T., Ramakrishnan, N.: Unsupervised spatial event detection in targeted domains with applications to civil unrest modeling. PLoS ONE 9(10), e110206 (2014)
Zhou, Y., Guan, X., Zhang, Z., Zhang, B.: Predicting the tendency of topic discussion on the online social networks using a dynamic probability model. In: Proceedings of the Hypertext 2008 Workshop on Collaboration and Collective Intelligence, pp. 7–11. ACM (2008)
Zhou, Y., Lu, T., Zhu, T., Chen, Z.: Environmental incidents detection from chinese microblog based on sentiment analysis. In: Zu, Q., Hu, B. (eds.) HCC 2016. LNCS, vol. 9567, pp. 849–854. Springer, Cham (2016). doi:10.1007/978-3-319-31854-7_88
Zhou, Y., Zhang, L., Liu, X., Zhang, Z., Bai, S., Zhu, T.: Predicting the trends of social events on Chinese social media. In: Cyberpsychology, Behavior and Social Networking (accepted)
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The authors gratefully acknowledges the generous support from Natural Science Foundation of Hubei Province (2016CFB208).
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Zhou, Y. et al. (2017). Social Events Forecasting in Microblogging. In: Zeng, Y., et al. Brain Informatics. BI 2017. Lecture Notes in Computer Science(), vol 10654. Springer, Cham. https://doi.org/10.1007/978-3-319-70772-3_22
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DOI: https://doi.org/10.1007/978-3-319-70772-3_22
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