Detecting Signals of Associations between Dietary Supplement Use and Mental Disorders from Twitter

Yefeng Wang, Yunpeng Zhao, Jiang Bian, Rui Zhang

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

1 Scopus citations

Abstract

In this study, we attempted to detect signals of association between dietary supplement use and mental disorders from Twitter. We collected tweets ranging from 2016 to 2017 which mention five dietary supplements. A case cohort of 257 users were identified by adapting a natural language processing method with further manually verified to have taken one supplement. We then randomly selected 257 users who had not taken any dietary supplement as the control cohort and compared the sentiment and mental health signals of their tweets to the case cohort. We have observed significant differences in the sad, anxious, and negative sentiment between the two cohorts. These results have shown that Twitter is a potential source for detecting signals of association between dietary supplement use and anxiety disorders, depression, and mood disorders.

Original languageEnglish (US)
Title of host publicationProceedings - 2018 IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages53-54
Number of pages2
ISBN (Electronic)9781538667774
DOIs
StatePublished - Jul 16 2018
Event6th IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018 - New York, United States
Duration: Jun 4 2018Jun 7 2018

Publication series

NameProceedings - 2018 IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018

Other

Other6th IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018
CountryUnited States
CityNew York
Period6/4/186/7/18

Bibliographical note

Funding Information:
This research was supported by National Center for Complementary & Integrative Health Award (R01AT009457) (PI: Zhang).

Publisher Copyright:
© 2018 IEEE.

Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.

Keywords

  • Twitter
  • mental health signals
  • natural language processing
  • social media

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