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arXiv · 1906.06465

Correlating Twitter Language with Community-Level Health Outcomes

Arno Schneuwly·Ralf Grubenmann·Séverine Rion Logean·Mark Cieliebak·Martin Jaggi
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Abstract

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.

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Arno Schneuwly, Ralf Grubenmann, Séverine Rion Logean, Mark Cieliebak, Martin Jaggi. 2019-06-13. Correlating Twitter Language with Community-Level Health Outcomes. https://arxiv.org/abs/1906.06465

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