Detecting Diabetes Risk from Social Media Activity

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

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).

Original languageEnglish (US)
Title of host publicationEMNLP 2018 - 9th International Workshop on Health Text Mining and Information Analysis, LOUHI 2018 - Proceedings of the Workshop
PublisherAssociation for Computational Linguistics (ACL)
Pages1-11
Number of pages11
ISBN (Electronic)9781948087742
StatePublished - 2018
Event9th International Workshop on Health Text Mining and Information Analysis, LOUHI 2018, co-located with EMNLP 2018 - Brussels, Belgium
Duration: Oct 31 2018 → …

Publication series

NameEMNLP 2018 - 9th International Workshop on Health Text Mining and Information Analysis, LOUHI 2018 - Proceedings of the Workshop

Conference

Conference9th International Workshop on Health Text Mining and Information Analysis, LOUHI 2018, co-located with EMNLP 2018
Country/TerritoryBelgium
CityBrussels
Period10/31/18 → …

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

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