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.
@article{arxiv.1906.06465,
title = {Correlating Twitter Language with Community-Level Health Outcomes},
author = {Arno Schneuwly and Ralf Grubenmann and Séverine Rion Logean and Mark Cieliebak and Martin Jaggi},
journal= {arXiv preprint arXiv:1906.06465},
year = {2019}
}
Comments
ACL SMM4H Workshop (Social Media Mining for Health Applications)