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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-18986
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dc.contributor.authorSchneuwly, Arno-
dc.contributor.authorGrubenmann, Ralf-
dc.contributor.authorLogean, Séverine Rion-
dc.contributor.authorCieliebak, Mark-
dc.contributor.authorJaggi, Martin-
dc.date.accessioned2019-12-19T13:49:58Z-
dc.date.available2019-12-19T13:49:58Z-
dc.date.issued2019-06-13-
dc.identifier.otherarXiv:1906.06465v1de_CH
dc.identifier.urihttps://arxiv.org/abs/1906.06465v1de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/18986-
dc.description.abstractWe 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.de_CH
dc.format.extent8de_CH
dc.language.isoende_CH
dc.publisherZHAW Zürcher Hochschule für Angewandte Wissenschaftende_CH
dc.rightshttp://creativecommons.org/licenses/by/4.0/de_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.subject.ddc610: Medizin und Gesundheitde_CH
dc.titleCorrelating twitter language with community-level health outcomesde_CH
dc.typeWorking Paper – Gutachten – Studiede_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Informatik (InIT)de_CH
dc.identifier.doi10.21256/zhaw-18986-
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.webfeedSoftware Systemsde_CH
zhaw.webfeedNatural Language Processingde_CH
zhaw.author.additionalNode_CH
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Schneuwly, A., Grubenmann, R., Logean, S. R., Cieliebak, M., & Jaggi, M. (2019). Correlating twitter language with community-level health outcomes. ZHAW Zürcher Hochschule für Angewandte Wissenschaften. https://doi.org/10.21256/zhaw-18986
Schneuwly, A. et al. (2019) Correlating twitter language with community-level health outcomes. ZHAW Zürcher Hochschule für Angewandte Wissenschaften. Available at: https://doi.org/10.21256/zhaw-18986.
A. Schneuwly, R. Grubenmann, S. R. Logean, M. Cieliebak, and M. Jaggi, “Correlating twitter language with community-level health outcomes,” ZHAW Zürcher Hochschule für Angewandte Wissenschaften, Jun. 2019. doi: 10.21256/zhaw-18986.
SCHNEUWLY, Arno, Ralf GRUBENMANN, Séverine Rion LOGEAN, Mark CIELIEBAK und Martin JAGGI, 2019. Correlating twitter language with community-level health outcomes [online]. ZHAW Zürcher Hochschule für Angewandte Wissenschaften. Verfügbar unter: https://arxiv.org/abs/1906.06465v1
Schneuwly, Arno, Ralf Grubenmann, Séverine Rion Logean, Mark Cieliebak, and Martin Jaggi. 2019. “Correlating Twitter Language with Community-Level Health Outcomes.” ZHAW Zürcher Hochschule für Angewandte Wissenschaften. https://doi.org/10.21256/zhaw-18986.
Schneuwly, Arno, et al. Correlating Twitter Language with Community-Level Health Outcomes. ZHAW Zürcher Hochschule für Angewandte Wissenschaften, 13 June 2019, https://doi.org/10.21256/zhaw-18986.


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