Affiliations: Department of Electrical, Computer, and Software Engineering, University of Ontario Institute of Technology, Oshawa, ON, Canada
Correspondence:
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Corresponding author: Somayyeh Aghababaei, Department of Electrical, Computer, and Software Engineering, University of Ontario Institute of Technology (UOIT), Oshawa, Ontario, Canada. E-mail: [email protected].
Abstract: While conventional crime prediction methods rely on historical crime records and geographical information of the location of interest, we pursue the question of whether a social media context can provide socio-behavior “signals” for a crime prediction problem. The hypothesis is that crowd publicly available data in Twitter may include predictive variables which can indicate changes in crime rates without being only limited to the availability of historical crime records of specific locations. We developed a prediction model for crime trend prediction, where the objective is to employ Twitter content to predict crime rate directions in a prospective time-frame. The model employs content, sentiment, and topics, as the predictive indicators to infer the changes of crime indexes. Since our problem has a sequential order, we propose a temporal topic detection model to infer predictive topics over time. The main challenge of topic detection over time is information evolution, in which data are more related when they are close in time rather than further apart. Our proposed topic detection model builds a dynamic vocabulary to detect emerging topics rather than considering a vocabulary in bulk. We applied our model on data collected from Chicago for crime trend prediction using historical tweets. The results have revealed the correlation between features extracted from the content as content-based features and the crime trends. Moreover, the results indicate the feasibility of our proposed temporal topic detection model in identifying the most predictive features over time compared to a static model without time consideration. We also studied the contribution of socio-economic indexes and temporal features as auxiliary features. The experiment shows the content-based features improve the prediction performance significantly compared to the auxiliary features. Overall, the study provides a deep insight into the correlation between language and crime trends and the impact of social data as an extra resource in providing predictive indicators.
Keywords: Twitter data analytics, temporal data analytics, text mining, topic modeling, sentiment analysis, social trend prediction
