Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-18986
Publication type: Working paper – expertise – study
Title: Correlating twitter language with community-level health outcomes
Authors : Schneuwly, Arno
Grubenmann, Ralf
Logean, Séverine Rion
Cieliebak, Mark
Jaggi, Martin
et. al : No
DOI : 10.21256/zhaw-18986
Extent : [8]
Issue Date: 13-Jun-2019
Publisher / Ed. Institution : ZHAW Zürcher Hochschule für Angewandte Wissenschaften
Other identifiers : arXiv:1906.06465v1
Language : English
Subject (DDC) : 004: Computer science
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.
URI: https://arxiv.org/abs/1906.06465v1
https://digitalcollection.zhaw.ch/handle/11475/18986
License (according to publishing contract) : CC BY 4.0: Attribution 4.0 International
Departement: School of Engineering
Organisational Unit: Institute of Applied Information Technology (InIT)
Appears in Collections:Publikationen School of Engineering

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