Detecting Diabetes Risk from Social Media Activity

Dane Bell, Egoitz Laparra, Aditya Kousik, Terron Ishihara, Mihai Surdeanu, Stephen Kobourov


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).
Anthology ID:
W18-5601
Volume:
Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis
Month:
October
Year:
2018
Address:
Brussels, Belgium
Venues:
EMNLP | Louhi | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1–11
Language:
URL:
https://www.aclweb.org/anthology/W18-5601
DOI:
10.18653/v1/W18-5601
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PDF:
http://aclanthology.lst.uni-saarland.de/W18-5601.pdf