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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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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