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Large-Scale Social Media Analysis Reveals Emotions Associated with Nonmedical Prescription Drug Use

BACKGROUND. The behaviors and emotions associated with and reasons for nonmedical prescription drug use (NMPDU) are not well-captured through traditional instruments such as surveys and insurance claims. Publicly available NMPDU-related posts on social media can potentially be leveraged to study the...

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Autores principales: Al-Garadi, Mohammed Ali, Yang, Yuan-Chi, Guo, Yuting, Kim, Sangmi, Love, Jennifer S., Perrone, Jeanmarie, Sarker, Abeed
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10449547/
https://www.ncbi.nlm.nih.gov/pubmed/37621877
http://dx.doi.org/10.34133/2022/9851989
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author Al-Garadi, Mohammed Ali
Yang, Yuan-Chi
Guo, Yuting
Kim, Sangmi
Love, Jennifer S.
Perrone, Jeanmarie
Sarker, Abeed
author_facet Al-Garadi, Mohammed Ali
Yang, Yuan-Chi
Guo, Yuting
Kim, Sangmi
Love, Jennifer S.
Perrone, Jeanmarie
Sarker, Abeed
author_sort Al-Garadi, Mohammed Ali
collection PubMed
description BACKGROUND. The behaviors and emotions associated with and reasons for nonmedical prescription drug use (NMPDU) are not well-captured through traditional instruments such as surveys and insurance claims. Publicly available NMPDU-related posts on social media can potentially be leveraged to study these aspects unobtrusively and at scale. METHODS. We applied a machine learning classifier to detect self-reports of NMPDU on Twitter and extracted all public posts of the associated users. We analyzed approximately 137 million posts from 87,718 Twitter users in terms of expressed emotions, sentiments, concerns, and possible reasons for NMPDU via natural language processing. RESULTS. Users in the NMPDU group express more negative emotions and less positive emotions, more concerns about family, the past, and body, and less concerns related to work, leisure, home, money, religion, health, and achievement compared to a control group (i.e., users who never reported NMPDU). NMPDU posts tend to be highly polarized, indicating potential emotional triggers. Gender-specific analyses show that female users in the NMPDU group express more content related to positive emotions, anticipation, sadness, joy, concerns about family, friends, home, health, and the past, and less about anger than males. The findings are consistent across distinct prescription drug categories (opioids, benzodiazepines, stimulants, and polysubstance). CONCLUSION. Our analyses of large-scale data show that substantial differences exist between the texts of the posts from users who self-report NMPDU on Twitter and those who do not, and between males and females who report NMPDU. Our findings can enrich our understanding of NMPDU and the population involved.
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spelling pubmed-104495472023-08-24 Large-Scale Social Media Analysis Reveals Emotions Associated with Nonmedical Prescription Drug Use Al-Garadi, Mohammed Ali Yang, Yuan-Chi Guo, Yuting Kim, Sangmi Love, Jennifer S. Perrone, Jeanmarie Sarker, Abeed Health Data Sci Article BACKGROUND. The behaviors and emotions associated with and reasons for nonmedical prescription drug use (NMPDU) are not well-captured through traditional instruments such as surveys and insurance claims. Publicly available NMPDU-related posts on social media can potentially be leveraged to study these aspects unobtrusively and at scale. METHODS. We applied a machine learning classifier to detect self-reports of NMPDU on Twitter and extracted all public posts of the associated users. We analyzed approximately 137 million posts from 87,718 Twitter users in terms of expressed emotions, sentiments, concerns, and possible reasons for NMPDU via natural language processing. RESULTS. Users in the NMPDU group express more negative emotions and less positive emotions, more concerns about family, the past, and body, and less concerns related to work, leisure, home, money, religion, health, and achievement compared to a control group (i.e., users who never reported NMPDU). NMPDU posts tend to be highly polarized, indicating potential emotional triggers. Gender-specific analyses show that female users in the NMPDU group express more content related to positive emotions, anticipation, sadness, joy, concerns about family, friends, home, health, and the past, and less about anger than males. The findings are consistent across distinct prescription drug categories (opioids, benzodiazepines, stimulants, and polysubstance). CONCLUSION. Our analyses of large-scale data show that substantial differences exist between the texts of the posts from users who self-report NMPDU on Twitter and those who do not, and between males and females who report NMPDU. Our findings can enrich our understanding of NMPDU and the population involved. 2022 2022-04-27 /pmc/articles/PMC10449547/ /pubmed/37621877 http://dx.doi.org/10.34133/2022/9851989 Text en https://creativecommons.org/licenses/by-nc/4.0/Exclusive Licensee Peking University Health Science Center. Distributed under a Creative Commons Attribution (https://creativecommons.org/licenses/by-nc/4.0/) License (CC BY 4.0).
spellingShingle Article
Al-Garadi, Mohammed Ali
Yang, Yuan-Chi
Guo, Yuting
Kim, Sangmi
Love, Jennifer S.
Perrone, Jeanmarie
Sarker, Abeed
Large-Scale Social Media Analysis Reveals Emotions Associated with Nonmedical Prescription Drug Use
title Large-Scale Social Media Analysis Reveals Emotions Associated with Nonmedical Prescription Drug Use
title_full Large-Scale Social Media Analysis Reveals Emotions Associated with Nonmedical Prescription Drug Use
title_fullStr Large-Scale Social Media Analysis Reveals Emotions Associated with Nonmedical Prescription Drug Use
title_full_unstemmed Large-Scale Social Media Analysis Reveals Emotions Associated with Nonmedical Prescription Drug Use
title_short Large-Scale Social Media Analysis Reveals Emotions Associated with Nonmedical Prescription Drug Use
title_sort large-scale social media analysis reveals emotions associated with nonmedical prescription drug use
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10449547/
https://www.ncbi.nlm.nih.gov/pubmed/37621877
http://dx.doi.org/10.34133/2022/9851989
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