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CRIME PREDICTION USING SOCIAL SENTIMENT AND SOCIO-FACTOR

Year 2018, Volume: 60 Issue: 1, 11 - 20, 31.07.2018

Abstract

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. 

References

  • 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.
Year 2018, Volume: 60 Issue: 1, 11 - 20, 31.07.2018

Abstract

References

  • 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.
There are 20 citations in total.

Details

Primary Language English
Journal Section Review Articles
Authors

Sakirin Tam This is me 0000-0003-4103-1797

Ö. Özgür Tanrıöver 0000-0003-0833-3494

Publication Date July 31, 2018
Submission Date November 11, 2017
Acceptance Date January 20, 2018
Published in Issue Year 2018 Volume: 60 Issue: 1

Cite

APA Tam, S., & Tanrıöver, Ö. Ö. (2018). CRIME PREDICTION USING SOCIAL SENTIMENT AND SOCIO-FACTOR. Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering, 60(1), 11-20.
AMA Tam S, Tanrıöver ÖÖ. CRIME PREDICTION USING SOCIAL SENTIMENT AND SOCIO-FACTOR. Commun.Fac.Sci.Univ.Ank.Series A2-A3: Phys.Sci. and Eng. July 2018;60(1):11-20.
Chicago Tam, Sakirin, and Ö. Özgür Tanrıöver. “CRIME PREDICTION USING SOCIAL SENTIMENT AND SOCIO-FACTOR”. Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering 60, no. 1 (July 2018): 11-20.
EndNote Tam S, Tanrıöver ÖÖ (July 1, 2018) CRIME PREDICTION USING SOCIAL SENTIMENT AND SOCIO-FACTOR. Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering 60 1 11–20.
IEEE S. Tam and Ö. Ö. Tanrıöver, “CRIME PREDICTION USING SOCIAL SENTIMENT AND SOCIO-FACTOR”, Commun.Fac.Sci.Univ.Ank.Series A2-A3: Phys.Sci. and Eng., vol. 60, no. 1, pp. 11–20, 2018.
ISNAD Tam, Sakirin - Tanrıöver, Ö. Özgür. “CRIME PREDICTION USING SOCIAL SENTIMENT AND SOCIO-FACTOR”. Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering 60/1 (July 2018), 11-20.
JAMA Tam S, Tanrıöver ÖÖ. CRIME PREDICTION USING SOCIAL SENTIMENT AND SOCIO-FACTOR. Commun.Fac.Sci.Univ.Ank.Series A2-A3: Phys.Sci. and Eng. 2018;60:11–20.
MLA Tam, Sakirin and Ö. Özgür Tanrıöver. “CRIME PREDICTION USING SOCIAL SENTIMENT AND SOCIO-FACTOR”. Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering, vol. 60, no. 1, 2018, pp. 11-20.
Vancouver Tam S, Tanrıöver ÖÖ. CRIME PREDICTION USING SOCIAL SENTIMENT AND SOCIO-FACTOR. Commun.Fac.Sci.Univ.Ank.Series A2-A3: Phys.Sci. and Eng. 2018;60(1):11-20.

Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering

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