2016/10/29 by Lakshika Balasuriya, Sanjaya Wijeratne, Derek Doran +1 · 1 voice
Computer Science · #cs.CL #cs.CY #cs.IR #cs.SI
published as The 2016 IEEE/ACM Int. Conf. on Advances in Social Networks Analysis and Mining. vol. 8, pp. 685-692. San Francisco, CA, USA (2016) · 8 pages, 9 figures, 2 tables, Published as a full paper at 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2016)
arxiv created 2016/10/29 · arxiv published 2016/10/29 · arxiv updated 2016/11/01
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.