Implicit group membership detection in online text: analysis and applications

Authors:
Jeffrey Ellen;Joan Kaina;Shibin Parameswaran
Affiliations:
Space and Naval Warfare Systems Center Pacific, United States Navy, San Diego, CA;Space and Naval Warfare Systems Center Pacific, United States Navy, San Diego, CA;Space and Naval Warfare Systems Center Pacific, United States Navy, San Diego, CA
Venue:
SBP'12 Proceedings of the 5th international conference on Social Computing, Behavioral-Cultural Modeling and Prediction
Year:
2012

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Abstract

Our thesis is that members of the same group have shared tendencies and nuances in communication style and substance, particularly online. In this paper, we dicuss some potential applications of accuarate authorship affiliation technology. We also discuss related work in similar author identification efforts and the research issues that currently exist when trying to perform automated authorship affiliation. We provide quantitative results from our recent Machine Learning experimenation using Support Vector Machines as some initial validation of our theory. In this paper, we applied our work towards the task of classifying website forum posts by the affiliation of their author. We discuss in detail the stylometric features we used to perform the automated classification and split the original features into individual groups to isolate their respective contributions and/or discriminating capability. Our results show promise towards automating group representation, an important first step in studying group formation.