  The socioeconomic status of people depends on a combination of individual
characteristics and environmental variables, thus its inference from online
behavioral data is a difficult task. Attributes like user semantics in
communication, habitat, occupation, or social network are all known to be
determinant predictors of this feature. In this paper we propose three
different data collection and combination methods to first estimate and, in
turn, infer the socioeconomic status of French Twitter users from their online
semantics. Our methods are based on open census data, crawled professional
profiles, and remotely sensed, expert annotated information on living
environment. Our inference models reach similar performance of earlier results
with the advantage of relying on broadly available datasets and of providing a
generalizable framework to estimate socioeconomic status of large numbers of
Twitter users. These results may contribute to the scientific discussion on
social stratification and inequalities, and may fuel several applications.
