Abstract
The outbreak of mass incidents severely affects the stability of society. If we can predict mass incidents in advance, we may find the solution to avoid the confliction in time. Some of the existing approaches rely on emotional modeling. Much research has been conducted on microblog incident detection using statistical models, like LASSO regression method, Dynamic Query Expansion (DQE) and so on. In this paper, we propose to combine sentiment analysis and statistical methods, and uses LASSO regression method for mass incidents prediction. Experiments on Qingdao demonstrated that our proposed approach achieves a good performance.
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References
Achrekar, H., Gandhe, A., Lazarus, R., Yu, S.H., Liu, B.: Predicting flu trends using twitter data. In: 2011 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), pp. 702–707. IEEE (2011)
Che, W., Li, Z., Liu, T.: LTP: a Chinese language technology platform. In: Proceedings of the 23rd International Conference on Computational Linguistics: Demonstrations, pp. 13–16. Association for Computational Linguistics (2010)
Chen, F., Neill, D.B.: Non-parametric scan statistics for event detection and forecasting in heterogeneous social media graphs. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1166–1175. ACM (2014)
Cheong, M., Lee, V.C.: A microblogging-based approach to terrorism informatics: exploration and chronicling civilian sentiment and response to terrorism events via twitter. Inf. Syst. Front. 13(1), 45–59 (2011)
Acknowledgements
The authors gratefully acknowledges the generous support from National High-tech R&D Program of China (2013AA01A606), National Basic Research Program of China (2014CB744600), and Key Research Program of Chinese Academy of Sciences (CAS) (KJZD-EWL04).
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Li, W., Zhou, Y., Lu, T., Zhu, T. (2016). Predicting Mass Incidents from Weibo. In: Zu, Q., Hu, B. (eds) Human Centered Computing. HCC 2016. Lecture Notes in Computer Science(), vol 9567. Springer, Cham. https://doi.org/10.1007/978-3-319-31854-7_96
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DOI: https://doi.org/10.1007/978-3-319-31854-7_96
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