# Social media fingerprints of unemployment

arXiv: https://arxiv.org/abs/1411.3140 · HTML: https://mlsanity.com/paper/1411.3140/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Nov 2014.

## Built on by (most cited first, 4 of 4)

- [Identifying latent activity behaviors and lifestyles using mobility data to describe urban dynamics](https://mlsanity.com/paper/2209.12095/) (2022)
- [A Scoping Review of Earth Observation and Machine Learning for Causal Inference: Implications for the Geography of Poverty](https://mlsanity.com/paper/2406.02584/) (2024)
- [Interpreting wealth distribution via poverty map inference using multimodal data](https://mlsanity.com/paper/2302.10793/) (2023)
- [Predicting Socio-Economic Well-being Using Mobile Apps Data: A Case Study of France](https://mlsanity.com/paper/2301.09986/) (2023)

## Abstract (arXiv)

Recent wide-spread adoption of electronic and pervasive technologies has enabled the study of human behavior at an unprecedented level, uncovering universal patterns underlying human activity, mobility, and inter-personal communication. In the present work, we investigate whether deviations from these universal patterns may reveal information about the socio-economical status of geographical regions. We quantify the extent to which deviations in diurnal rhythm, mobility patterns, and communication styles across regions relate to their unemployment incidence. For this we examine a country-scale publicly articulated social media dataset, where we quantify individual behavioral features from over 145 million geo-located messages distributed among more than 340 different Spanish economic regions, inferred by computing communities of cohesive mobility fluxes. We find that regions exhibiting more diverse mobility fluxes, earlier diurnal rhythms, and more correct grammatical styles display lower unemployment rates. As a result, we provide a simple model able to produce accurate, easily interpretable reconstruction of regional unemployment incidence from their social-media digital fingerprints alone. Our results show that cost-effective economical indicators can be built based on publicly-available social media datasets.