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Wednesday, July 29 • 13:31 - 15:00
"Crime Trend Prediction Using Social Data"

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Authors: Masoud Makrehchi and Somayyeh Aghababaei

In this study we present how user-generated content in social media can be leveraged in crime trend prediction. We introduce a topic-based model for predicting crime trend. While conventional trend prediction methods are limited to availability of labelled data, our proposed model generates training data without human interventions by labeling with the background knowledge inferred from the domain. A temporal topic identification model was proposed to capture the most “novel” topics with addressing topics evolution. The results of experiments reveal the correlation between inferred topics and crime index changes. While predictability is high in some specific crime types, it could be variant depends on the incidents. In overall, the study provides insight into the impacts of social data in providing predictive indicators for real world problems.

avatar for Somayyeh (Bahar) Aghababaei

Somayyeh (Bahar) Aghababaei

PhD student, University of Ontario Institute of Technology (UOIT)
I am a second year PhD student at UOIT, where I am advised by Dr. Masoud Makrehchi. I am broadly interested in semi-supervised learning, domain adaptation, and transfer learning, and their applications in link prediction, language modeling, socio-economic trend prediction, sentiment analysis, and role detection.

Wednesday July 29, 2015 13:31 - 15:00
(8th Floor) TRS 2-166 (Ted Rogers School of Management) 55 Dundas Street West, Toronto, ON M5G 2C3

Attendees (13)