Word Embeddings to Enhance Twitter Gang Member Profile Identification

TitleWord Embeddings to Enhance Twitter Gang Member Profile Identification
Publication TypeConference Paper
Year of Publication2016
AuthorsSanjaya Wijeratne, Lakshika Balasuriya, Derek Doran, Amit Sheth
Conference NameIJCAI Workshop on Semantic Machine Learning (SML 2016)
Date Published07/2016
PublisherCEUR-WS
Conference LocationNew York City, NY
KeywordsGang Activity Understanding, social media analysis, Street Gangs, Twitter Profile Identification, Word Embeddings
Abstract

Gang affiliates have joined the masses who use social media to share thoughts and actions publicly. Interestingly, they use this public medium to express recent illegal actions, to intimidate others, and to share outrageous images and statements. Agencies able to unearth these profiles may thus be able to anticipate, stop, or hasten the investigation of gang-related crimes. This paper investigates the use of word embeddings to help identify gang members on Twitter. Building on our previous work, we generate word embeddings that translate what Twitter users post in their profile descriptions, tweets, profile images, and linked YouTube content to a real vector format amenable for machine learning classification. Our experimental results show that pre-trained word embeddings can boost the accuracy of supervised learning algorithms trained over gang members’ social media posts.