Finding Street Gang Members on Twitter
This work addresses the challenge for law enforcement agencies in detecting gang members on social media, though it is incremental as it builds on existing classification methods with new data.
The paper tackled the problem of automatically identifying street gang members on Twitter by curating a large dataset of verifiable profiles and training supervised classifiers based on linguistic and multimedia features, achieving a promising F1 score with a low false positive rate.
Most street gang members use Twitter to intimidate others, to present outrageous images and statements to the world, and to share recent illegal activities. Their tweets may thus be useful to law enforcement agencies to discover clues about recent crimes or to anticipate ones that may occur. Finding these posts, however, requires a method to discover gang member Twitter profiles. This is a challenging task since gang members represent a very small population of the 320 million Twitter users. This paper studies the problem of automatically finding gang members on Twitter. It outlines a process to curate one of the largest sets of verifiable gang member profiles that have ever been studied. A review of these profiles establishes differences in the language, images, YouTube links, and emojis gang members use compared to the rest of the Twitter population. Features from this review are used to train a series of supervised classifiers. Our classifier achieves a promising F1 score with a low false positive rate.