Automatic crime prediction using events extracted from twitter posts

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7227 LNCS, Issue , 2012, Pages 231-238

  Wang, Xiaofeng ^a   Gerber, Matthew S ^a   Brown, Donald E ^a  

^a University of Virginia   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BASELINE MODELS; DEMOGRAPHIC INFORMATION; DIMENSIONALITY REDUCTION; EVALUATION RESULTS; GEO-SPATIAL; LATENT DIRICHLET ALLOCATION; LINEAR MODELING; NATURAL LANGUAGES; SEMANTIC ANALYSIS; SOCIAL MEDIA;

CRIME; FORECASTING; SEMANTICS; SOCIAL NETWORKING (ONLINE); STATISTICS;

COMPUTER CRIME;

EID: 84859142978     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-29047-3_28     Document Type: Conference Paper

References (14)

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)
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)
3. Blei, D., Carin, L., Dunson, D.: Probabilistic topic models. IEEE Signal Processing Magazine 27(6), 55-65 (2010)
4. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993-1022 (2003)
5. Bollen, J., Mao, H., Zeng, X.: Twitter mood predicts the stock market. Journal of Computational Science (2011)
6. Chainey, S., Tompson, L., Uhlig, S.: The utility of hotspot mapping for predicting spatial patterns of crime. Security Journal 21, 428 (2008)
7. Eck, J., Chainey, S., Cameron, J., Leitner, M., Wilson, R.: Mapping crime: Understanding hot spots (2005)
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)
9. Gildea, D., Jurafsky, D.: Automatic labeling of semantic roles. Computational Linguistics 28, 245-288 (2002)
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)
11. Màrquez, L., Carreras, X., Litkowski, K.C., Stevenson, S.: Semantic role labeling: an introduction to the special issue. Comput. Linguist. 34(2), 145-159 (2008)
12. Mohler, G.O., Short, M.B., Brantingham, P.J., Schoenberg, F.P., Tita, G.E.: Self-exciting point process modeling of crime. Journal of the American Statistical Association 106(493), 100-108 (2011)
13. Punyakanok, V., Roth, D., Yih, W.T.: The importance of syntactic parsing and inference in semantic role labeling. Comput. Linguist. 34(2), 257-287 (2008)
14. Wang, X., Brown, D.E.: The spatio-temporal generalized additive model for criminal incidents. In: ISI (2011)