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.