Illicit, Hidden Advertisements on Twitter

Modern communication is ubiquitous thanks to the advances in mobile devices and the spread of social networks across the world. It allows people to share ideas, opinions, and experiences. With an ever grown number of users, third party entities promote their services through online advertisements aimed to attract more customers. These services can be even illegal, making this kind of ads deceptive. In this work, we study the use of tweeter for human trafficking criminal network. Mining data from tweets, primarily to identify hidden information is a complicated process, which even requires human intervention. Thus, more sophisticated natural language methods are necessary. In our experiments, the term punters had the most explicit message related to human trafficking services, we find some phrases that depending on the context could lead to criminal activities, and we show that temporal analysis seems an exciting research area to identify behavioral patterns which certainly can help to combat this kind of criminal networks.