Extracting social values and group identities from social media text data

This paper presents preliminary results on the extraction of group identities from social media data using topic models and a rich form of sentiment analysis that is designed to correspond to psychologically-validated emotional states. Our approach is based upon the sociological notion that group identity forms the basis for behavioral change [1]. We begin by inferring social values from social media text data by combining information regarding topic content and sentiment. Next, groups are inferred as a latent variable mediating between individual social media authors and social values. A topic model is proposed, extending the Ailment Topic Aspect Model (ATAM) used by Paul and Dredze [2], and applied to a large set of blog data extracted from the Media Cloud [3] daily updates. We also provide a qualitative and quantitative analysis of model outputs.