Finding Street Gang Members on Twitter

TL;DR

This paper presents a method to identify street gang members on Twitter by curating a large dataset, analyzing their online behavior, and training classifiers that can distinguish them from the general user base.

Contribution

It introduces a large, verified dataset of gang member profiles and develops a supervised classification approach based on linguistic and multimedia features.

Findings

Classifier achieves high F1 score with low false positives

Gang members use distinctive language, images, and emojis

Method aids law enforcement in identifying potential gang activity

Abstract

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.