Toxicity on Social Media During the 2022 Mpox Public Health Emergency: Quantitative Study of Topical and Network Dynamics.

BACKGROUND

Toxicity on social media, encompassing behaviors such as harassment, bullying, hate speech, and the dissemination of misinformation, has become a pressing social concern in the digital age. Its prevalence intensifies during periods of social crises and unrest, eroding a sense of safety and community. Such toxic environments can adversely impact the mental well-being of those exposed and further deepen societal divisions and polarization. The 2022 mpox outbreak, initially called "monkeypox" but later renamed to reduce stigma and address societal concerns, provides a relevant context for this issue.

OBJECTIVE

In this study, we conducted a comprehensive analysis of the toxic online discourse surrounding the 2022 mpox outbreak. We aimed to dissect its origins, characterize its nature and content, trace its dissemination patterns, and assess its broader societal implications, with the goal of providing insights that can inform strategies to mitigate such toxicity in future crises.

METHODS

We collected >1.6 million unique tweets and analyzed them with 5 dimensions: context, extent, content, speaker, and intent. Using topic modeling based on bidirectional encoder representations from transformers and social network community clustering, we delineated the toxic dynamics on Twitter.

RESULTS

By categorizing topics, we identified 5 high-level categories in the toxic online discourse on Twitter, including disease (20,281/43,521, 46.6%), health policy and health care (8400/43,521, 19.3%), homophobia (10,402/43,521, 23.9%), politics (2611/43,521, 6%), and racism (1784/43,521, 4.1%). Across these categories, users displayed negativity or controversial views on the mpox outbreak, highlighting the escalating political tensions and the weaponization of stigma during this infodemic. Through the toxicity diffusion networks of mentions (17,437 vertices with 3628 clusters), retweets (59,749 vertices with 3015 clusters), and the top users with the highest in-degree centrality, we found that retweets of toxic content were widespread, while influential users rarely engaged with or countered this toxicity through retweets.

CONCLUSIONS

Our study introduces a comprehensive workflow that combines topical and network analyses to decode emerging social issues during crises. By tracking topical dynamics, we can track the changing popularity of toxic content on the internet, providing a better understanding of societal challenges. Network dynamics highlight key social media influencers and their intentions, suggesting that engaging with these central figures in toxic discourse can improve crisis communication and guide policy making.