Correlating Twitter Language with Community-Level Health Outcomes

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 stateof-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.

Written Justifications are Key to Aggregate Crowdsourced Forecasts

“…Previous work includes both predictive models (input: language samples, output: some attribute about the author) and models that yield useful insights (input: language samples and attributes of the authors, output: differentiating language features depending on the attributes). Among many others, previous research has studied gender and age Nguyen et al, 2014;Peersman et al, 2011), political ideology (Iyyer et al, 2014;Preoţiuc-Pietro et al, 2017), health outcomes (Schneuwly et al, 2019), and personality traits (Schwartz et al, 2013). In this paper, we do not profile forecasters.…”