Identifying Criminal Suspects on Social Networks: A Vocabulary-Based Method

Abstract

Identifying suspects of crimes on social networks is one of the most relevant tasks in the analysis of this type of network. Most of the computational methods focused on this task involve supervised machine learning, and, therefore, require previously labeled datasets that inform, among the registered people, messages and/or conversations, which ones are suspects. However, in practice, this type of information is not available, for several reasons, among which, it is rare or even protected by secrecy guaranteed by law. This limitation makes it very difficult to effectively use these methods in real situations. Hence, the present work raises the hypothesis that the use of a controlled vocabulary on the field of application can make it possible the identification of suspects in social networks, without the need for previously labeled datasets. In order to search for experimental evidence that points to the validity of the hypothesis raised, this article proposes a generic method that uses a controlled vocabulary with categorized terms, according to a certain domain (e.g., pedophilia, cyberbullying, terrorism, etc.), to analyze messages exchanged on social networks, in order to identify criminal suspects. The results obtained in a preliminary experiment in pedophilia domain showed signs of adequacy of the proposed method.