Identification of Myths and Misinformation About Treatment for Opioid Use Disorder on Social Media: Infodemiology Study
BackgroundHealth misinformation and myths about treatment for opioid use disorder (OUD) are present on social media and contribute to challenges in preventing drug overdose deaths. However, no systematic, quantitative methodology exists to identify what types of misinformatio...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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JMIR Publications
2024-02-01
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| Series: | JMIR Formative Research |
| Online Access: | https://formative.jmir.org/2024/1/e44726 |
| _version_ | 1797299276829687808 |
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| author | Mai ElSherief Steven Sumner Vikram Krishnasamy Christopher Jones Royal Law Akadia Kacha-Ochana Lyna Schieber Munmun De Choudhury |
| author_facet | Mai ElSherief Steven Sumner Vikram Krishnasamy Christopher Jones Royal Law Akadia Kacha-Ochana Lyna Schieber Munmun De Choudhury |
| author_sort | Mai ElSherief |
| collection | DOAJ |
| description |
BackgroundHealth misinformation and myths about treatment for opioid use disorder (OUD) are present on social media and contribute to challenges in preventing drug overdose deaths. However, no systematic, quantitative methodology exists to identify what types of misinformation are being shared and discussed.
ObjectiveWe developed a multistage analytic pipeline to assess social media posts from Twitter (subsequently rebranded as X), YouTube, Reddit, and Drugs-Forum for the presence of health misinformation about treatment for OUD.
MethodsOur approach first used document embeddings to identify potential new statements of misinformation from known myths. These statements were grouped into themes using hierarchical agglomerative clustering, and public health experts then reviewed the results for misinformation.
ResultsWe collected a total of 19,953,599 posts discussing opioid-related content across the aforementioned platforms. Our multistage analytic pipeline identified 7 main clusters or discussion themes. Among a high-yield data set of posts (n=303) for further public health expert review, these included discussion about potential treatments for OUD (90/303, 29.8%), the nature of addiction (68/303, 22.5%), pharmacologic properties of substances (52/303, 16.9%), injection drug use (36/303, 11.9%), pain and opioids (28/303, 9.3%), physical dependence of medications (22/303, 7.2%), and tramadol use (7/303, 2.3%). A public health expert review of the content within each cluster identified the presence of misinformation and myths beyond those used as seed myths to initialize the algorithm.
ConclusionsIdentifying and addressing misinformation through appropriate communication strategies could be an increasingly important component of preventing overdose deaths. To further this goal, we developed and tested an approach to aid in the identification of myths and misinformation about OUD from large-scale social media content. |
| first_indexed | 2024-03-07T22:48:15Z |
| format | Article |
| id | doaj.art-1ba57f6a9fe040a2892f13a09dbfe540 |
| institution | Directory Open Access Journal |
| issn | 2561-326X |
| language | English |
| last_indexed | 2024-03-07T22:48:15Z |
| publishDate | 2024-02-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | JMIR Formative Research |
| spelling | doaj.art-1ba57f6a9fe040a2892f13a09dbfe5402024-02-23T14:00:34ZengJMIR PublicationsJMIR Formative Research2561-326X2024-02-018e4472610.2196/44726Identification of Myths and Misinformation About Treatment for Opioid Use Disorder on Social Media: Infodemiology StudyMai ElSheriefhttps://orcid.org/0000-0003-4718-5201Steven Sumnerhttps://orcid.org/0000-0002-3805-7235Vikram Krishnasamyhttps://orcid.org/0000-0002-1203-3198Christopher Joneshttps://orcid.org/0000-0001-8598-2100Royal Lawhttps://orcid.org/0000-0002-5746-5373Akadia Kacha-Ochanahttps://orcid.org/0000-0003-1980-5098Lyna Schieberhttps://orcid.org/0000-0002-6774-5854Munmun De Choudhuryhttps://orcid.org/0000-0002-8939-264X BackgroundHealth misinformation and myths about treatment for opioid use disorder (OUD) are present on social media and contribute to challenges in preventing drug overdose deaths. However, no systematic, quantitative methodology exists to identify what types of misinformation are being shared and discussed. ObjectiveWe developed a multistage analytic pipeline to assess social media posts from Twitter (subsequently rebranded as X), YouTube, Reddit, and Drugs-Forum for the presence of health misinformation about treatment for OUD. MethodsOur approach first used document embeddings to identify potential new statements of misinformation from known myths. These statements were grouped into themes using hierarchical agglomerative clustering, and public health experts then reviewed the results for misinformation. ResultsWe collected a total of 19,953,599 posts discussing opioid-related content across the aforementioned platforms. Our multistage analytic pipeline identified 7 main clusters or discussion themes. Among a high-yield data set of posts (n=303) for further public health expert review, these included discussion about potential treatments for OUD (90/303, 29.8%), the nature of addiction (68/303, 22.5%), pharmacologic properties of substances (52/303, 16.9%), injection drug use (36/303, 11.9%), pain and opioids (28/303, 9.3%), physical dependence of medications (22/303, 7.2%), and tramadol use (7/303, 2.3%). A public health expert review of the content within each cluster identified the presence of misinformation and myths beyond those used as seed myths to initialize the algorithm. ConclusionsIdentifying and addressing misinformation through appropriate communication strategies could be an increasingly important component of preventing overdose deaths. To further this goal, we developed and tested an approach to aid in the identification of myths and misinformation about OUD from large-scale social media content.https://formative.jmir.org/2024/1/e44726 |
| spellingShingle | Mai ElSherief Steven Sumner Vikram Krishnasamy Christopher Jones Royal Law Akadia Kacha-Ochana Lyna Schieber Munmun De Choudhury Identification of Myths and Misinformation About Treatment for Opioid Use Disorder on Social Media: Infodemiology Study JMIR Formative Research |
| title | Identification of Myths and Misinformation About Treatment for Opioid Use Disorder on Social Media: Infodemiology Study |
| title_full | Identification of Myths and Misinformation About Treatment for Opioid Use Disorder on Social Media: Infodemiology Study |
| title_fullStr | Identification of Myths and Misinformation About Treatment for Opioid Use Disorder on Social Media: Infodemiology Study |
| title_full_unstemmed | Identification of Myths and Misinformation About Treatment for Opioid Use Disorder on Social Media: Infodemiology Study |
| title_short | Identification of Myths and Misinformation About Treatment for Opioid Use Disorder on Social Media: Infodemiology Study |
| title_sort | identification of myths and misinformation about treatment for opioid use disorder on social media infodemiology study |
| url | https://formative.jmir.org/2024/1/e44726 |
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