An Integrated Model for User Attribute Discovery: A Case Study on Political Affiliation Identification

Abstract

Discovering user demographic attributes from social media is a problem of considerable interest. The problem setting can be generalized to include three components-users, topics and behaviors. In recent studies on this problem, however, the behavior between users and topics are not effectively incorporated. In our work, we proposed an integrated unsupervised model which takes into consideration all the three components integral to the task. Furthermore, our model incorporates collaborative filtering with probabilistic matrix factorization to solve the data sparsity problem, a computational challenge common to all such tasks. We evaluated our method on a case study of user political affiliation identification, and compared against state-of-the-art baselines. Our model achieved an accuracy of 70.1% for user party detection task.

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“…Some of the attributes targeted for extraction focus on demographic related information, such as gender/age (Koppel et al, 2002;Mukherjee and Liu, 2010;Burger et al, 2011;Van Durme, 2012;, race/ethnicity (Pennacchiotti and Popescu, 2011;Eisenstein et al, 2011;Rao et al, 2011;, location (Bamman et al, 2014), yet other aspects are mined as well, among them emotion and sentiment , personality types (Schwartz et al, 2013;, user political affiliation (Cohen and Ruths, 2013;Volkova and Durme, 2015), mental health diagnosis (Coppersmith et al, 2015) and even lifestyle choices such as coffee preference (Pennacchiotti and Popescu, 2011). The task is typically approached from a machine learning perspective, with data originating from a variety of user generated content, most often microblogs (Pennacchiotti and Popescu, 2011;Coppersmith et al, 2015;, article com-ments to news stories or op-ed pieces (Riordan et al, 2014), social posts (originating from sites such as Facebook, MySpace, Google+) (Gong et al, 2012), or discussion forums on particular topics (Gottipati et al, 2014). Classification labels are then assigned either based on manual annotations , self identified user attributes (Pennacchiotti and Popescu, 2011), affiliation with a given discussion forum type, or online surveys set up to link a social media user identification to the responses provided (Schwartz et al, 2013).…”