Misinformation and Public Health Messaging in the Early Stages of the Mpox Outbreak: Mapping the Twitter Narrative With Deep Learning
BackgroundShortly after the worst of the COVID-19 pandemic, an outbreak of mpox introduced another critical public health emergency. Like the COVID-19 pandemic, the mpox outbreak was characterized by a rising prevalence of public health misinformation on social media, through...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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JMIR Publications
2023-06-01
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| Series: | Journal of Medical Internet Research |
| Online Access: | https://www.jmir.org/2023/1/e43841 |
| _version_ | 1797734047749767168 |
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| author | Andy Edinger Danny Valdez Eric Walsh-Buhi Jennifer S Trueblood Lorenzo Lorenzo-Luaces Lauren A Rutter Johan Bollen |
| author_facet | Andy Edinger Danny Valdez Eric Walsh-Buhi Jennifer S Trueblood Lorenzo Lorenzo-Luaces Lauren A Rutter Johan Bollen |
| author_sort | Andy Edinger |
| collection | DOAJ |
| description |
BackgroundShortly after the worst of the COVID-19 pandemic, an outbreak of mpox introduced another critical public health emergency. Like the COVID-19 pandemic, the mpox outbreak was characterized by a rising prevalence of public health misinformation on social media, through which many US adults receive and engage with news. Digital misinformation continues to challenge the efforts of public health officials in providing accurate and timely information to the public. We examine the evolving topic distributions of social media narratives during the mpox outbreak to map the tension between rapidly diffusing misinformation and public health communication.
ObjectiveThis study aims to observe topical themes occurring in a large-scale collection of tweets about mpox using deep learning.
MethodsWe leveraged a data set comprised of all mpox-related tweets that were posted between May 7, 2022, and July 23, 2022. We then applied Sentence Bidirectional Encoder Representations From Transformers (S-BERT) to the content of each tweet to generate a representation of its content in high-dimensional vector space, where semantically similar tweets will be located closely together. We projected the set of tweet embeddings to a 2D map by applying principal component analysis and Uniform Manifold Approximation Projection (UMAP). Finally, we group these data points into 7 topical clusters using k-means clustering and analyze each cluster to determine its dominant topics. We analyze the prevalence of each cluster over time to evaluate longitudinal thematic changes.
ResultsOur deep-learning pipeline revealed 7 distinct clusters of content: (1) cynicism, (2) exasperation, (3) COVID-19, (4) men who have sex with men, (5) case reports, (6) vaccination, and (7) World Health Organization (WHO). Clusters that largely communicated erroneous or irrelevant information began earlier and grew faster, reaching a wider audience than later communications by official instances and health officials.
ConclusionsWithin a few weeks of the first reported mpox cases, an avalanche of mostly false, misleading, irrelevant, or damaging information started to circulate on social media. Official institutions, including the WHO, acted promptly, providing case reports and accurate information within weeks, but were overshadowed by rapidly spreading social media chatter. Our results point to the need for real-time monitoring of social media content to optimize responses to public health emergencies. |
| first_indexed | 2024-03-12T12:38:35Z |
| format | Article |
| id | doaj.art-a1905eb85e2f42f9b5289f513acfb13d |
| institution | Directory Open Access Journal |
| issn | 1438-8871 |
| language | English |
| last_indexed | 2024-03-12T12:38:35Z |
| publishDate | 2023-06-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | Journal of Medical Internet Research |
| spelling | doaj.art-a1905eb85e2f42f9b5289f513acfb13d2023-08-29T00:01:29ZengJMIR PublicationsJournal of Medical Internet Research1438-88712023-06-0125e4384110.2196/43841Misinformation and Public Health Messaging in the Early Stages of the Mpox Outbreak: Mapping the Twitter Narrative With Deep LearningAndy Edingerhttps://orcid.org/0000-0001-8159-2121Danny Valdezhttps://orcid.org/0000-0002-2355-9881Eric Walsh-Buhihttps://orcid.org/0000-0002-1690-5352Jennifer S Truebloodhttps://orcid.org/0000-0002-9019-6574Lorenzo Lorenzo-Luaceshttps://orcid.org/0000-0002-8882-0243Lauren A Rutterhttps://orcid.org/0000-0002-8852-7602Johan Bollenhttps://orcid.org/0000-0001-7031-9293 BackgroundShortly after the worst of the COVID-19 pandemic, an outbreak of mpox introduced another critical public health emergency. Like the COVID-19 pandemic, the mpox outbreak was characterized by a rising prevalence of public health misinformation on social media, through which many US adults receive and engage with news. Digital misinformation continues to challenge the efforts of public health officials in providing accurate and timely information to the public. We examine the evolving topic distributions of social media narratives during the mpox outbreak to map the tension between rapidly diffusing misinformation and public health communication. ObjectiveThis study aims to observe topical themes occurring in a large-scale collection of tweets about mpox using deep learning. MethodsWe leveraged a data set comprised of all mpox-related tweets that were posted between May 7, 2022, and July 23, 2022. We then applied Sentence Bidirectional Encoder Representations From Transformers (S-BERT) to the content of each tweet to generate a representation of its content in high-dimensional vector space, where semantically similar tweets will be located closely together. We projected the set of tweet embeddings to a 2D map by applying principal component analysis and Uniform Manifold Approximation Projection (UMAP). Finally, we group these data points into 7 topical clusters using k-means clustering and analyze each cluster to determine its dominant topics. We analyze the prevalence of each cluster over time to evaluate longitudinal thematic changes. ResultsOur deep-learning pipeline revealed 7 distinct clusters of content: (1) cynicism, (2) exasperation, (3) COVID-19, (4) men who have sex with men, (5) case reports, (6) vaccination, and (7) World Health Organization (WHO). Clusters that largely communicated erroneous or irrelevant information began earlier and grew faster, reaching a wider audience than later communications by official instances and health officials. ConclusionsWithin a few weeks of the first reported mpox cases, an avalanche of mostly false, misleading, irrelevant, or damaging information started to circulate on social media. Official institutions, including the WHO, acted promptly, providing case reports and accurate information within weeks, but were overshadowed by rapidly spreading social media chatter. Our results point to the need for real-time monitoring of social media content to optimize responses to public health emergencies.https://www.jmir.org/2023/1/e43841 |
| spellingShingle | Andy Edinger Danny Valdez Eric Walsh-Buhi Jennifer S Trueblood Lorenzo Lorenzo-Luaces Lauren A Rutter Johan Bollen Misinformation and Public Health Messaging in the Early Stages of the Mpox Outbreak: Mapping the Twitter Narrative With Deep Learning Journal of Medical Internet Research |
| title | Misinformation and Public Health Messaging in the Early Stages of the Mpox Outbreak: Mapping the Twitter Narrative With Deep Learning |
| title_full | Misinformation and Public Health Messaging in the Early Stages of the Mpox Outbreak: Mapping the Twitter Narrative With Deep Learning |
| title_fullStr | Misinformation and Public Health Messaging in the Early Stages of the Mpox Outbreak: Mapping the Twitter Narrative With Deep Learning |
| title_full_unstemmed | Misinformation and Public Health Messaging in the Early Stages of the Mpox Outbreak: Mapping the Twitter Narrative With Deep Learning |
| title_short | Misinformation and Public Health Messaging in the Early Stages of the Mpox Outbreak: Mapping the Twitter Narrative With Deep Learning |
| title_sort | misinformation and public health messaging in the early stages of the mpox outbreak mapping the twitter narrative with deep learning |
| url | https://www.jmir.org/2023/1/e43841 |
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