Automatically estimating a user's socioeconomic profile from their language use in social media can significantly help social science research and various downstream applications ranging from business to politics. The current paper presents the first study where user cognitive structure is used to build a predictive model of income. In particular, we first develop a classifier using a weakly supervised learning framework to automatically time-tag tweets as past, present, or future. We quantify a user's overall temporal orientation based on their distribution of tweets, and use it to build a predictive model of income. Our analysis uncovers a correlation between future temporal orientation and income. Finally, we measure the predictive power of future temporal orientation on income by performing regression.
REF are the first to use user cognitive structure in terms of the user's overall temporal orientation to predict income uncovering a correlation between future temporal orientation and income.
Temporal Orientation of Tweets for Predicting Income of Users
ACL
Computer Science