Paper: Modeling the Use of Graffiti Style Features to Signal Social Relations within a Multi-Domain Learning Paradigm

ACL ID E14-1012
Title Modeling the Use of Graffiti Style Features to Signal Social Relations within a Multi-Domain Learning Paradigm
Venue Annual Meeting of The European Chapter of The Association of Computational Linguistics
Session Main Conference
Year 2014
Authors

In this paper, we present a series of experiments in which we analyze the usage of graffiti style features for signaling personal gang identification in a large, online street gangs forum, with an accuracy as high as 83% at the gang alliance level and 72% for the specific gang. We then build on that result in predicting how members of different gangs signal the relationship between their gangs within threads where they are interacting with one another, with a predictive accuracy as high as 66% at this thread composition prediction task. Our work demonstrates how graffiti style features signal social identity both in terms of personal group affiliation and between group alliances and oppositions. When we predict thread composition by modeling identity and relationship s...