Understanding cognitive styles from user-generated social media content

Abstract

The linguistic analyses on easily accessible, user-generated social media content offers great opportunities to identify individual characteristics such as their cognitive styles. In this paper, We explore the potential to use social media content to identify individuals' cognitive styles. We first employed crowdsourcing to collect Twitter users' cognitive styles using standard psychometric instruments. Then, we extracted the linguistic features of their social media postings. Leveraging these features, we build prediction models that provide estimates of cognitive styles through statistical regression and classification. We find that user generated content in social media provide useful information for characterizing people's cognitive styles. The models' performance indicates that the cognitive styles automatically inferred from social media are good proxies for the ground truth, and hence provides a promising and scalable way to automatically identify a large number of people's cognitive styles without reaching them individually.