Citation

Balasuriya L, Wijeratne S, Doran D, Sheth A. Proc. IEEE ACM Int. Conf. Adv. Soc. Netw. Anal. Min. 2016; 206: 685-692.

Affiliation

Kno.e.sis Center, Wright State University, Dayton OH, USA.

Copyright

(Copyright © 2016, IEEE Computer Society, Conference Publishing Services (CPS))

DOI

10.1109/ASONAM.2016.7752311

PMID

28713880

PMCID

PMC5508795

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.

Language: en

Keywords

Gang Activity Understanding; Social Media Analysis; Street Gangs; Twitter Profile Identification