@inproceedings{bell-etal-2018-detecting,
title = "Detecting Diabetes Risk from Social Media Activity",
author = "Bell, Dane and
Laparra, Egoitz and
Kousik, Aditya and
Ishihara, Terron and
Surdeanu, Mihai and
Kobourov, Stephen",
editor = "Lavelli, Alberto and
Minard, Anne-Lyse and
Rinaldi, Fabio",
booktitle = "Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis",
month = oct,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-5601",
doi = "10.18653/v1/W18-5601",
pages = "1--11",
abstract = "This work explores the detection of individuals{'} risk of type 2 diabetes mellitus (T2DM) directly from their social media (Twitter) activity. Our approach extends a deep learning architecture with several contributions: following previous observations that language use differs by gender, it captures and uses gender information through domain adaptation; it captures recency of posts under the hypothesis that more recent posts are more representative of an individual{'}s current risk status; and, lastly, it demonstrates that in this scenario where activity factors are sparsely represented in the data, a bag-of-word neural network model using custom dictionaries of food and activity words performs better than other neural sequence models. Our best model, which incorporates all these contributions, achieves a risk-detection F1 of 41.9, considerably higher than the baseline rate (36.9).",
}
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<abstract>This work explores the detection of individuals’ risk of type 2 diabetes mellitus (T2DM) directly from their social media (Twitter) activity. Our approach extends a deep learning architecture with several contributions: following previous observations that language use differs by gender, it captures and uses gender information through domain adaptation; it captures recency of posts under the hypothesis that more recent posts are more representative of an individual’s current risk status; and, lastly, it demonstrates that in this scenario where activity factors are sparsely represented in the data, a bag-of-word neural network model using custom dictionaries of food and activity words performs better than other neural sequence models. Our best model, which incorporates all these contributions, achieves a risk-detection F1 of 41.9, considerably higher than the baseline rate (36.9).</abstract>
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%0 Conference Proceedings
%T Detecting Diabetes Risk from Social Media Activity
%A Bell, Dane
%A Laparra, Egoitz
%A Kousik, Aditya
%A Ishihara, Terron
%A Surdeanu, Mihai
%A Kobourov, Stephen
%Y Lavelli, Alberto
%Y Minard, Anne-Lyse
%Y Rinaldi, Fabio
%S Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis
%D 2018
%8 October
%I Association for Computational Linguistics
%C Brussels, Belgium
%F bell-etal-2018-detecting
%X This work explores the detection of individuals’ risk of type 2 diabetes mellitus (T2DM) directly from their social media (Twitter) activity. Our approach extends a deep learning architecture with several contributions: following previous observations that language use differs by gender, it captures and uses gender information through domain adaptation; it captures recency of posts under the hypothesis that more recent posts are more representative of an individual’s current risk status; and, lastly, it demonstrates that in this scenario where activity factors are sparsely represented in the data, a bag-of-word neural network model using custom dictionaries of food and activity words performs better than other neural sequence models. Our best model, which incorporates all these contributions, achieves a risk-detection F1 of 41.9, considerably higher than the baseline rate (36.9).
%R 10.18653/v1/W18-5601
%U https://aclanthology.org/W18-5601
%U https://doi.org/10.18653/v1/W18-5601
%P 1-11
Markdown (Informal)
[Detecting Diabetes Risk from Social Media Activity](https://aclanthology.org/W18-5601) (Bell et al., Louhi 2018)
ACL
- Dane Bell, Egoitz Laparra, Aditya Kousik, Terron Ishihara, Mihai Surdeanu, and Stephen Kobourov. 2018. Detecting Diabetes Risk from Social Media Activity. In Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis, pages 1–11, Brussels, Belgium. Association for Computational Linguistics.