@inproceedings{schneuwly-etal-2019-correlating,
title = "Correlating {T}witter Language with Community-Level Health Outcomes",
author = "Schneuwly, Arno and
Grubenmann, Ralf and
Rion Logean, S{\'e}verine and
Cieliebak, Mark and
Jaggi, Martin",
editor = "Weissenbacher, Davy and
Gonzalez-Hernandez, Graciela",
booktitle = "Proceedings of the Fourth Social Media Mining for Health Applications ({\#}SMM4H) Workshop {\&} Shared Task",
month = aug,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-3210",
doi = "10.18653/v1/W19-3210",
pages = "71--78",
abstract = "We study how language on social media is linked to mortal 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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<abstract>We study how language on social media is linked to mortal 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.</abstract>
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%0 Conference Proceedings
%T Correlating Twitter Language with Community-Level Health Outcomes
%A Schneuwly, Arno
%A Grubenmann, Ralf
%A Rion Logean, Séverine
%A Cieliebak, Mark
%A Jaggi, Martin
%Y Weissenbacher, Davy
%Y Gonzalez-Hernandez, Graciela
%S Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F schneuwly-etal-2019-correlating
%X We study how language on social media is linked to mortal 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.
%R 10.18653/v1/W19-3210
%U https://aclanthology.org/W19-3210
%U https://doi.org/10.18653/v1/W19-3210
%P 71-78
Markdown (Informal)
[Correlating Twitter Language with Community-Level Health Outcomes](https://aclanthology.org/W19-3210) (Schneuwly et al., ACL 2019)
ACL
- Arno Schneuwly, Ralf Grubenmann, Séverine Rion Logean, Mark Cieliebak, and Martin Jaggi. 2019. Correlating Twitter Language with Community-Level Health Outcomes. In Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task, pages 71–78, Florence, Italy. Association for Computational Linguistics.