CRIME PREDICTION USING SOCIAL SENTIMENT AND SOCIO-FACTOR

Öz Crime prediction becomes very important trend and a key technique in crime analysis to identify the optimal patrol strategy for police department. Many researchers have found number of techniques and solutions to analyze crime, using data mining techniques. These studies can help to speed up and computerize the process of crime analysis processes.  However, the pattern of crime is flexible, it always changes and grows. With social media, user posts and discusses event publicly. These textual data of every user has contextual information of user’s daily activities. These posts generate unstructured data that can be used for data prediction. As shown by previous research, twitter sentiment enable to predict crime in Chicago, United States. However, existed model on crime prediction was incorporating the use of socio factors. Therefore, the study aims to model crime prediction using social media content with additional socio-factors. The research approach is consisted of a combination of sentiment analysis from Twitter and social-factors with Kernel Density Estimation. Lexicon-base methods will be applied for sentiment analysis, and the model evaluation is measured with the help of logistic regression. 

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Matthew, S. G., Predicting crime using twitter and kernel density estimation, Decision Support Systems, 61 (2014), 115–125.

Twitter, TWITTER USAGE / COMPANY FACTS, Retrieved from http://www.twitter.com Retried on November 1, 2017

Salim A. and Omer, E., Cybercrime Profiling: Text mining techniques to detect and predict criminal activities in microblog posts, International Conference on Intelligent Systems: Theories and Applications (SITA), (2015) 1-5.

Vieweg, S., Hughes, A. L., Starbird K. and Palen, L., Microblogging during two natural hazards events: what twitter may contribute to situational awareness, SIGCHI Conference on Human Factors in Computing Systems, (2010) 1079–1088.

Tumasjan, A., Sprenger, T. O., Sandner, P. Q. and Welpe, I. M., Predicting elections with twitter: What 140 characters reveal about political sentiment. ICWSM, 10 (2010),178–185.

Xiaofeng, W., Matthew S. G. and Donald, E. B., Automatic crime prediction using events extracted from twitter posts. Social Computing, Behavioral-Cultural Modeling and Prediction, (2015) 231–238.

Sathyadevan, S., Devan M. and Surya, S., Gangadharan, Crime analysis and prediction using data mining, Networks Soft Computing (ICNSC), (2014) 406–412.

Chainey, S., Tompson, L. and Uhlig, S., The utility of hotspot mapping for predicting spatial patterns of crime, Security Journal, 21(2008), 4–28.

Caplan, J.M. and Kennedy, L.W., Risk terrain modeling compendium. Rutgers Center on Public Security, Newark, (2011).

Mohammad A.B. and Matthew, S.G., Predicting Crime with Routine Activity Patterns Inferred from Social Media. International Conference on Systems, Man, and Cybernetics – SMC, (2016), 1233-1238.

Mohler, G.O., Short, M.B., Brantingham, P.J., Schoenberg F.P. and Tita, G.E., Self-Exciting Point Process Modeling of Crime. Journal of the American Statistical Association, 106 (2011), 100-108.

Xue, Y. and Brown, D.E., Spatial analysis with preference specification of latent decision makers for criminal event prediction, Decision Support Systems, 41 (2006), 560–573.

Kalampokis, E., Tambouris, E., and Tarabanis, K., Understanding the predictive power of social media, Internet Research, 23 (2013).

Culotta, A. and Huberman. B., Towards detecting influenza epidemics by analyzing Twitter messages, Proceedings of the First Workshop on Social Media Analytics, ACM, (2010) 115–122.

Franch, F., Wisdom of the crowds 2: 2010 UK election prediction with social media, Journal of Information Technology & Politics, 10 (2013), 57–71.

Wang, X., Brown, D. and Gerber, M., Spatio-temporalmodeling of criminal incidents using geographic, demographic, and Twitter-derived information, Intelligence and Security Informatics. Lecture Notes in Computer Science, IEEE Press, (2012).

Asur, S. and Huberman, B., Predicting the future with social media, IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, IEEE, (2010) 492–499.

Earle, P.S., Bowden, D.C. and Guy, M, Twitter earthquake detection: earthquake monitoring in a social world, Annals of Geophysics, 54 (2012).

Choi, H,. and Varian, H., Predicting the present with Google Trends, The Economic Record, 88 (2012), 2–9.

Bollen, J., Mao H., Zeng, X., Twitter mood predicts the stock market, Journal of Computational Science, 2(2011), 1–8.