Emoticons and Phrases: Status Symbols in Social Media

There is a sociolinguistic interest in studying the social power dynamics that arise on online social networks and how these are reflected in their users ’ use of lan-guage. Online social power prediction can also be used to build tools for marketing and political campaigns that help them build an audience. Existing work has focused on finding correlations between status and linguistic fea-tures in email, Wikipedia discussions, and court hearings. While a few studies have tried predicting status on the basis of language on Twitter, they have proved less fruit-ful. We derive a rich set of features from literature in a variety of disciplines and build classifiers that assign Twitter users to different levels of status based on their language use. Using various metrics such as number of followers and Klout score, we achieve a classification ac-curacy of individual users as high as 82.4%. In a second step, we reached up to 71.6 % accuracy on the task of pre-dicting the more powerful user in a dyadic conversation. We find that the manner in which powerful users write differs from low status users in a number of different ways: not only in the extent to which they deviate from their usual writing habits when conversing with others but also in pronoun use, language complexity, sentiment expression, and emoticon use. By extending our analysis to Facebook, we also assess the generalisability of our results and discuss differences and similarities between these two sites.