Using emotions to predict user interest areas in online social networks

We examine the relation between the emotions users express on social networks and their perceived areas of interests, based on a sample of Twitter users. Our methodology relies on training machine learning models to classify the emotions expressed in tweets, according to Ekman's six high-level emotions. We then used raters, sourced from Amazon's Mechanical Turk, to examine several Twitter profiles and to determine whether the profile owner is interested in various areas, including sports, movies, technology and computing, politics, news, economics, science, arts, health and religion. We find that the propensity of a user to express various emotions correlates with their perceived degree of interest in various areas. We present several models that use the emotional distribution of a Twitter user, as reflected by their tweets, to predict whether they are interested or disinterested in a topic or to determine their degree of interest in a topic.