JMIR Publications

ABSTRACT

Background:

Like the COVID-19 pandemic, the recent global monkeypox outbreak was characterized by the rising prevalence of public health misinformation on social media. This highlights the continuing challenges faced by public health officials in providing accurate and timely information to the public. We examine the evolving topic distributions of social media narratives during the monkeypox outbreak to observe the tension between rapidly diffusing misinformation and public health communication.

Objective:

To analyze Twitter discussions surrounding the global monkeypox outbreak beginning in May-July 2022.

Methods:

We collected all monkeypox-related Tweets posted between May 7, 2022 and July 23, 2022. We then applied Sentence Bi-directional Encoder from Transformers (S-BERT) to tweet content to generate representations of their 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 their dominant topics. We analyze the prevalence of each cluster over time to evaluate longitudinal changes in the data.

Results:

We discovered 7 distinct clusters of topical content. 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.

Conclusions:

Within a few weeks of the first reported monkeypox 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.