Identifying vehicle descriptions in microblogging text with the aim of reducing or predicting crime

Abstract

Could Social Media, and in particular, microblogs such as Twitter, play a part in helping to track criminal movement? The aim of this paper is to narrow the focus of this broader problem of using social media to crowdsource information to assist in the fight against crime, to the specific problem of identifying the description of vehicles in microblog text. As this problem has many aspects, especially in terms of data gathering and identification, an initial search is performed on preset keywords and the resulting database is tagged. The tags are then analysed to determine which features are the most common. Topic models are then run on the data to determine if any useful keyword can be found for further searches and initial statistics are recorded as a baseline for further processing. Our primary concern is establishing the common content of the relevant Tweets. The result could be used both for help with data collection as well as with feature selection when learning classification algorithms for data mining.

Estimating the Spatial Distribution of Crime Events around a Football Stadium from Georeferenced Tweets

“…For studies focused exclusively on crime analysis, the spatial relationship between the density of social media (primarily tweets) and crime locations has been explored with no concern for the semantic message embedded in tweets [28][29][30][31][32]. If social media usage is sufficient for a particular study area, its usage may have some predictive value [29,30].…”

Modeling Temporal-Spatial Correlations for Crime Prediction

“…For example, researchers show that there is correlation between the characteristics of a population and the rate of violent crimes [31]. The author in [32] is able to discover a correlation between reported crime census statistics from the South African Police Service and crime events discussed in tweets. While authors in [33] conclude that there is a positive effect of symbolic racism on both preventive and punitive penalties.…”

Multi-Type Urban Crime Prediction

“…The first category of works related to our study is traditional statistical, data mining and seismic analysis methods for crime predictions. The first group is statistical methods (Gruenewald et al 2006;Green, Staerkle, and Sears 2006;Featherstone 2013). For example, researchers show that there is correlation between the characteristics of a population and the rate of violent crimes (Gruenewald et al 2006).…”