# Correlating Twitter Language with Community-Level Health Outcomes

arXiv: https://arxiv.org/abs/1906.06465 · HTML: https://mlsanity.com/paper/1906.06465/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jun 2019.

## Built on by (most cited first, 1 of 1)

- [Written Justifications are Key to Aggregate Crowdsourced Forecasts](https://mlsanity.com/paper/2109.07017/) (2021)

## Abstract (arXiv)

We study how language on social media is linked to diseases such as atherosclerotic heart disease (AHD), diabetes and various types of cancer. Our proposed model leverages state-of-the-art sentence embeddings, followed by a regression model and clustering, without the need of additional labelled data. It allows to predict community-level medical outcomes from language, and thereby potentially translate these to the individual level. The method is applicable to a wide range of target variables and allows us to discover known and potentially novel correlations of medical outcomes with life-style aspects and other socioeconomic risk factors.