# Prediction of employment and unemployment rates from Twitter daily rhythms in the US

## TL;DR

This study demonstrates that daily activity patterns on Twitter can be used to predict employment and unemployment rates across US counties, offering a digital approach to economic monitoring.

## Contribution

The paper introduces a method to extract employment indicators from Twitter activity rhythms, linking digital footprints to macroeconomic employment data.

## Key findings

- Activity patterns correlate with employment rates ($0.46\pm0.02$)
- Patterns are inversely related to unemployment ($-0.34\pm0.02$)
- Digital traces can complement traditional economic surveys

## Abstract

By modeling macro-economical indicators using digital traces of human activities on mobile or social networks, we can provide important insights to processes previously assessed via paper-based surveys or polls only. We collected aggregated workday activity timelines of US counties from the normalized number of messages sent in each hour on the online social network Twitter. In this paper, we show how county employment and unemployment statistics are encoded in the daily rhythm of people by decomposing the activity timelines into a linear combination of two dominant patterns. The mixing ratio of these patterns defines a measure for each county, that correlates significantly with employment ($0.46\pm0.02$) and unemployment rates ($-0.34\pm0.02$). Thus, the two dominant activity patterns can be linked to rhythms signaling presence or lack of regular working hours of individuals. The analysis could provide policy makers a better insight into the processes governing employment, where problems could not only be identified based on the number of officially registered unemployed, but also on the basis of the digital footprints people leave on different platforms.