Misinformation and Public Health Messaging in the Early Stages of the Mpox Outbreak: Mapping the Twitter Narrative with Deep Learning.
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Abstract
Shortly 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.To observe topical themes occuring in a large-scale collection oftweets about Mpox using deep-learning.We leveraged a dataset comprised of all MPox related tweets that were posted between May 7, 2022 and July 23, 2022. We then applied Sentence Bi-directional Encoder 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 project the set of tweet embeddings to a two-dimensional map by applying Principal Component Analysis (PCA) and Uniform Manifold Approximation Projection (UMAP). Finally, we group these datapoints 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.Our deep learning pipeline revealed 7 distinct clusters of content: (1) Cynicism, (2) Exasperation, (3) COVID-19, (4) MSM, (5) Case Reports, (6) Vaccination, (7) 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.Within 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 World Health Organization (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.