# Location, Occupation, and Semantics based Socioeconomic Status Inference on Twitter

## TL;DR

This paper presents methods to infer the socioeconomic status of French Twitter users by combining online semantics with census, professional, and environmental data, achieving comparable results to prior studies with more accessible datasets.

## Contribution

It introduces three novel data collection and combination methods for socioeconomic inference from Twitter, leveraging open datasets and environmental information.

## Key findings

- Models achieve similar performance to previous work.
- Framework is generalizable and relies on broadly available data.
- Supports social stratification and inequality research.

## Abstract

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.