Automatic Crime Prediction Using Events Extracted from Twitter Posts
Prior work on criminal incident prediction has relied primarily on the historical crime record and various geospatial and demographic information sources. Although promising, these models do not take into account the rich and rapidly expanding social media context that surrounds incidents of interest. This paper presents a preliminary investigation of Twitter-based criminal incident prediction. Our approach is based on the automatic semantic analysis and understanding of natural language Twitter posts, combined with dimensionality reduction via latent Dirichlet allocation and prediction via linear modeling. We tested our model on the task of predicting future hit-and-run crimes. Evaluation results indicate that the model comfortably outperforms a baseline model that predicts hit-and-run incidents uniformly across all days.
KeywordsKernel Density Estimation Latent Dirichlet Allocation Sentiment Analysis Latent Dirichlet Allocation Model Generalize Linear Regression Model
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- 1.Asur, S., Huberman, B.: Predicting the future with social media. In: 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, pp. 492–499. IEEE (2010)Google Scholar
- 2.Bermingham, A., Smeaton, A.: 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), Asian Federation of Natural Language Processing, Chiang Mai, Thailand, pp. 2–10 (November 2011)Google Scholar
- 3.Blei, D., Carin, L., Dunson, D.: Probabilistic topic models. IEEE Signal Processing Magazine 27(6), 55–65 (2010)Google Scholar
- 5.Bollen, J., Mao, H., Zeng, X.: Twitter mood predicts the stock market. Journal of Computational Science (2011)Google Scholar
- 6.Chainey, S., Tompson, L., Uhlig, S.: The utility of hotspot mapping for predicting spatial patterns of crime. Security Journal 21, 428 (2008)Google Scholar
- 7.Eck, J., Chainey, S., Cameron, J., Leitner, M., Wilson, R.: Mapping crime: Understanding hot spots (2005)Google Scholar
- 8.Gerber, M., Chai, J., Meyers, A.: The role of implicit argumentation in nominal SRL. In: Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, pp. 146–154. Association for Computational Linguistics, Boulder (2009)Google Scholar
- 10.Howard, P.N., Duffy, A., Freelon, D., Hussain, M., Mari, W., Mazaid, M.: Opening closed regimes: What was the role of social media during the arab spring? Tech. rep., Project on Information Technology and Political Islam, University of Washington, Seattle (January 2011)Google Scholar
- 14.Wang, X., Brown, D.E.: The spatio-temporal generalized additive model for criminal incidents. In: ISI (2011)Google Scholar