Detecting associations between dietary supplement intake and sentiments within mental disorder tweets

Yefeng Wang; Yunpeng Zhao; Jianqiu Zhang; Jiang Bian; Rui Zhang

doi:10.1177/1460458219867231

Detecting associations between dietary supplement intake and sentiments within mental disorder tweets

Yefeng Wang, Yunpeng Zhao, Jianqiu Zhang, Jiang Bian, Rui Zhang

Pharmaceutical Care and Health Systems

Research output: Contribution to journal › Article › peer-review

9 Scopus citations

Abstract

Many patients with mental disorders take dietary supplement, but their use patterns remain unclear. In this study, we developed a method to detect signals of associations between dietary supplement intake and mental disorder in Twitter data. We developed an annotated dataset and trained a convolutional neural network classifier that can identify language use pattern of dietary supplement intake with an F1-score of 0.899, a precision of 0.900, and a recall of 0.900. Using the classifier, we discovered that melatonin and vitamin D were the most commonly used supplements among Twitter users who self-diagnosed mental disorders. Sentiment analysis using Linguistic Inquiry and Word Count has shown that among Twitter users who posted mental disorder self-diagnosis, users who indicated supplement intake are more active and express more negative emotions and fewer positive emotions than those who have not mentioned supplement intake.

Original language	English (US)
Pages (from-to)	803-815
Number of pages	13
Journal	Health Informatics Journal
Volume	26
Issue number	2
DOIs	https://doi.org/10.1177/1460458219867231
State	Published - Jun 1 2020

Bibliographical note

Funding Information:
https://orcid.org/0000-0003-2153-8187 Wang Yefeng University of Minnesota, USA https://orcid.org/0000-0002-5771-3373 Zhao Yunpeng University of Florida, USA Zhang Jianqiu University of Minnesota, USA https://orcid.org/0000-0002-2238-5429 Bian Jiang University of Florida, USA Zhang Rui University of Minnesota, USA Rui Zhang, Institute for Health Informatics and College of Pharmacy, University of Minnesota, 8-100 Phillips-Wangensteen Building, 516 Delaware Street SE, Minneapolis, MN 55455, USA. Email: zhan1386@umn.edu 9 2019 1460458219867231 © The Author(s) 2019 2019 SAGE Publications This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License ( http://www.creativecommons.org/licenses/by-nc/4.0/ ) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages ( https://us.sagepub.com/en-us/nam/open-access-at-sage ). Many patients with mental disorders take dietary supplement, but their use patterns remain unclear. In this study, we developed a method to detect signals of associations between dietary supplement intake and mental disorder in Twitter data. We developed an annotated dataset and trained a convolutional neural network classifier that can identify language use pattern of dietary supplement intake with an F1-score of 0.899, a precision of 0.900, and a recall of 0.900. Using the classifier, we discovered that melatonin and vitamin D were the most commonly used supplements among Twitter users who self-diagnosed mental disorders. Sentiment analysis using Linguistic Inquiry and Word Count has shown that among Twitter users who posted mental disorder self-diagnosis, users who indicated supplement intake are more active and express more negative emotions and fewer positive emotions than those who have not mentioned supplement intake. dietary supplement mental health natural language processing sentiment analysis social media NSF Award 1734134 National Center for Complementary & Integrative Health of the National Institutes of Health R01AT009457 edited-state corrected-proof The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or National Science Foundation. Declaration of conflicting interests The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Research reported in this publication was supported by the National Center for Complementary & Integrative Health and the Office of Dietary Supplements (ODS) of the National Institutes of Health under Award Number R01AT009457 (PI: Zhang) and NSF Award #1734134 (PI: Bian). ORCID iDs Yefeng Wang https://orcid.org/0000-0003-2153-8187 Yunpeng Zhao https://orcid.org/0000-0002-5771-3373 Jiang Bian https://orcid.org/0000-0002-2238-5429

Funding Information:
The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or National Science Foundation. The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Research reported in this publication was supported by the National Center for Complementary & Integrative Health and the Office of Dietary Supplements (ODS) of the National Institutes of Health under Award Number R01AT009457 (PI: Zhang) and NSF Award #1734134 (PI: Bian).

Publisher Copyright:
© The Author(s) 2019.

Keywords

dietary supplement
mental health
natural language processing
sentiment analysis
social media

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1177/1460458219867231

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title = "Detecting associations between dietary supplement intake and sentiments within mental disorder tweets",

abstract = "Many patients with mental disorders take dietary supplement, but their use patterns remain unclear. In this study, we developed a method to detect signals of associations between dietary supplement intake and mental disorder in Twitter data. We developed an annotated dataset and trained a convolutional neural network classifier that can identify language use pattern of dietary supplement intake with an F1-score of 0.899, a precision of 0.900, and a recall of 0.900. Using the classifier, we discovered that melatonin and vitamin D were the most commonly used supplements among Twitter users who self-diagnosed mental disorders. Sentiment analysis using Linguistic Inquiry and Word Count has shown that among Twitter users who posted mental disorder self-diagnosis, users who indicated supplement intake are more active and express more negative emotions and fewer positive emotions than those who have not mentioned supplement intake.",

keywords = "dietary supplement, mental health, natural language processing, sentiment analysis, social media",

author = "Yefeng Wang and Yunpeng Zhao and Jianqiu Zhang and Jiang Bian and Rui Zhang",

note = "Funding Information: https://orcid.org/0000-0003-2153-8187 Wang Yefeng University of Minnesota, USA https://orcid.org/0000-0002-5771-3373 Zhao Yunpeng University of Florida, USA Zhang Jianqiu University of Minnesota, USA https://orcid.org/0000-0002-2238-5429 Bian Jiang University of Florida, USA Zhang Rui University of Minnesota, USA Rui Zhang, Institute for Health Informatics and College of Pharmacy, University of Minnesota, 8-100 Phillips-Wangensteen Building, 516 Delaware Street SE, Minneapolis, MN 55455, USA. Email: zhan1386@umn.edu 9 2019 1460458219867231 {\textcopyright} The Author(s) 2019 2019 SAGE Publications This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License ( http://www.creativecommons.org/licenses/by-nc/4.0/ ) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages ( https://us.sagepub.com/en-us/nam/open-access-at-sage ). Many patients with mental disorders take dietary supplement, but their use patterns remain unclear. In this study, we developed a method to detect signals of associations between dietary supplement intake and mental disorder in Twitter data. We developed an annotated dataset and trained a convolutional neural network classifier that can identify language use pattern of dietary supplement intake with an F1-score of 0.899, a precision of 0.900, and a recall of 0.900. Using the classifier, we discovered that melatonin and vitamin D were the most commonly used supplements among Twitter users who self-diagnosed mental disorders. Sentiment analysis using Linguistic Inquiry and Word Count has shown that among Twitter users who posted mental disorder self-diagnosis, users who indicated supplement intake are more active and express more negative emotions and fewer positive emotions than those who have not mentioned supplement intake. dietary supplement mental health natural language processing sentiment analysis social media NSF Award 1734134 National Center for Complementary & Integrative Health of the National Institutes of Health R01AT009457 edited-state corrected-proof The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or National Science Foundation. Declaration of conflicting interests The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Research reported in this publication was supported by the National Center for Complementary & Integrative Health and the Office of Dietary Supplements (ODS) of the National Institutes of Health under Award Number R01AT009457 (PI: Zhang) and NSF Award #1734134 (PI: Bian). ORCID iDs Yefeng Wang https://orcid.org/0000-0003-2153-8187 Yunpeng Zhao https://orcid.org/0000-0002-5771-3373 Jiang Bian https://orcid.org/0000-0002-2238-5429 Funding Information: The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or National Science Foundation. The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Research reported in this publication was supported by the National Center for Complementary & Integrative Health and the Office of Dietary Supplements (ODS) of the National Institutes of Health under Award Number R01AT009457 (PI: Zhang) and NSF Award #1734134 (PI: Bian). Publisher Copyright: {\textcopyright} The Author(s) 2019.",

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doi = "10.1177/1460458219867231",

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T1 - Detecting associations between dietary supplement intake and sentiments within mental disorder tweets

AU - Wang, Yefeng

AU - Zhao, Yunpeng

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AU - Bian, Jiang

AU - Zhang, Rui

N1 - Funding Information: https://orcid.org/0000-0003-2153-8187 Wang Yefeng University of Minnesota, USA https://orcid.org/0000-0002-5771-3373 Zhao Yunpeng University of Florida, USA Zhang Jianqiu University of Minnesota, USA https://orcid.org/0000-0002-2238-5429 Bian Jiang University of Florida, USA Zhang Rui University of Minnesota, USA Rui Zhang, Institute for Health Informatics and College of Pharmacy, University of Minnesota, 8-100 Phillips-Wangensteen Building, 516 Delaware Street SE, Minneapolis, MN 55455, USA. Email: zhan1386@umn.edu 9 2019 1460458219867231 © The Author(s) 2019 2019 SAGE Publications This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License ( http://www.creativecommons.org/licenses/by-nc/4.0/ ) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages ( https://us.sagepub.com/en-us/nam/open-access-at-sage ). Many patients with mental disorders take dietary supplement, but their use patterns remain unclear. In this study, we developed a method to detect signals of associations between dietary supplement intake and mental disorder in Twitter data. We developed an annotated dataset and trained a convolutional neural network classifier that can identify language use pattern of dietary supplement intake with an F1-score of 0.899, a precision of 0.900, and a recall of 0.900. Using the classifier, we discovered that melatonin and vitamin D were the most commonly used supplements among Twitter users who self-diagnosed mental disorders. Sentiment analysis using Linguistic Inquiry and Word Count has shown that among Twitter users who posted mental disorder self-diagnosis, users who indicated supplement intake are more active and express more negative emotions and fewer positive emotions than those who have not mentioned supplement intake. dietary supplement mental health natural language processing sentiment analysis social media NSF Award 1734134 National Center for Complementary & Integrative Health of the National Institutes of Health R01AT009457 edited-state corrected-proof The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or National Science Foundation. Declaration of conflicting interests The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Research reported in this publication was supported by the National Center for Complementary & Integrative Health and the Office of Dietary Supplements (ODS) of the National Institutes of Health under Award Number R01AT009457 (PI: Zhang) and NSF Award #1734134 (PI: Bian). ORCID iDs Yefeng Wang https://orcid.org/0000-0003-2153-8187 Yunpeng Zhao https://orcid.org/0000-0002-5771-3373 Jiang Bian https://orcid.org/0000-0002-2238-5429 Funding Information: The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or National Science Foundation. The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Research reported in this publication was supported by the National Center for Complementary & Integrative Health and the Office of Dietary Supplements (ODS) of the National Institutes of Health under Award Number R01AT009457 (PI: Zhang) and NSF Award #1734134 (PI: Bian). Publisher Copyright: © The Author(s) 2019.

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N2 - Many patients with mental disorders take dietary supplement, but their use patterns remain unclear. In this study, we developed a method to detect signals of associations between dietary supplement intake and mental disorder in Twitter data. We developed an annotated dataset and trained a convolutional neural network classifier that can identify language use pattern of dietary supplement intake with an F1-score of 0.899, a precision of 0.900, and a recall of 0.900. Using the classifier, we discovered that melatonin and vitamin D were the most commonly used supplements among Twitter users who self-diagnosed mental disorders. Sentiment analysis using Linguistic Inquiry and Word Count has shown that among Twitter users who posted mental disorder self-diagnosis, users who indicated supplement intake are more active and express more negative emotions and fewer positive emotions than those who have not mentioned supplement intake.

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Detecting associations between dietary supplement intake and sentiments within mental disorder tweets

Abstract

Bibliographical note

Keywords

UN SDGs

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