Modeling and analysis of identity threat behaviors through text mining of identity theft stories

Identity theft, fraud, and abuse are problems affecting the entire society. Identity theft is often a “gateway” crime, as criminals use stolen or fraudulent identities to steal money, claim eligibility for services, hack into networks without authorization, and so on. The available data describing identity crimes and their aftermath are often in the form of recorded stories and reports by the news press, fraud examiners, and law enforcement. All of these sources are unstructured. In order to analyze identity theft data, this research proposes an approach which involves the novel collection of online news stories and reports on the topic of identity theft. Our approach pre-processes the raw text and extracts semi-structured information automatically, using text mining techniques. This paper presents statistical analysis of behavioral patterns and resources used by thieves and fraudsters to commit identity theft, including the identity attributes commonly linked to identity crimes, resources thieves employ to conduct identity crimes, and temporal patterns of criminal behavior. Furthermore, the automatically extracted information is validated against manually investigated news stories. Analyses of these results increase empirical understanding of identity threat behaviors, offer early warning signs of identity theft, and thwart future identity theft crimes.