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

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.