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

arXiv: https://arxiv.org/abs/1901.05389 · HTML: https://mlsanity.com/paper/1901.05389/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jan 2019.

## Built on by (most cited first, 3 of 3)

- [Lady and the Tramp Nextdoor: Online Manifestations of Economic Inequalities in the Nextdoor Social Network](https://mlsanity.com/paper/2304.05232/) (2023)
- [Networks and Identity Drive Geographic Properties of the Diffusion of Linguistic Innovation](https://mlsanity.com/paper/2202.04842/) (2022)
- [Interpreting wealth distribution via poverty map inference using multimodal data](https://mlsanity.com/paper/2302.10793/) (2023)

## Abstract (arXiv)

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.