Background: Online toxicity, encompassing behaviors such as harassment, bullying, hate speech, and the dissemination of misinformation, has become a pressing social concern in the digital age. The 2022 Mpox outbreak, initially termed "Monkeypox" but subsequently renamed to mitigate associated stigmas and societal concerns, serves as a poignant backdrop to this issue. Objective: In this research, we undertake a comprehensive analysis of the toxic online discourse surrounding the 2022 Mpox outbreak. Our objective is 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 more than 1.6 million unique tweets and analyzed them from five dimensions, including context, extent, content, speaker, and intent. Utilizing BERT-based topic modeling and social network community clustering, we delineated the toxic dynamics on Twitter. Results: We identified five high-level topic categories in the toxic online discourse on Twitter, including disease (46.6%), health policy and healthcare (19.3%), homophobia (23.9%), politics (6.0%), and racism (4.1%). Through the toxicity diffusion networks of mentions, retweets, and the top users, we found that retweets of toxic content were widespread, while influential users rarely engaged with or countered this toxicity through retweets. Conclusions: By tracking topical dynamics, we can track the changing popularity of toxic content online, providing a better understanding of societal challenges. Network dynamics spotlight key social media influencers and their intents, indicating that addressing these central figures in toxic discourse can enhance crisis communication and inform policy-making.

U.S. political discourse seems to have fissioned into discrete bubbles, each reflecting its own distorted image of the world. Many blame machine-learning algorithms that purportedly maximize "engagement"--serving up content that keeps YouTube or Facebook users watching videos or scrolling through their feeds--for radicalizing users or strengthening their partisanship. Sociologist Shoshana Zuboff15 even argues that "surveillance capitalism" uses optimized algorithmic feedback for "automated behavioral modification" at scale, writing the "music" that users then "dance" to. There is debate whether such algorithms in fact maximize engagement (their objective functions also typically contain other desiderata). More recent research3 offers an alternative explanation, suggesting that people consume this content because they want it, independent of the algorithm.

With increased connectivity comes increased concerns - especially for parents with children that are active online. Parents obviously want to shield their children from the horrific experiences we all hear, see, and read about. However, it takes more than just telling children to not share personal information to protect them from toxic online behavior such as bullying, hate speech, and sexual predators. It's a frightening new world online, especially for kids and the stats are eye-opening. The need for a better all-encompassing solution becomes magnified when you consider the fact that oftentimes, it's the children who are so tech-savvy their parents are unaware of their actions, much less the new online behavioral norms of gameplay or slang and worse yet many of those actions are difficult to trace.

Artificial intelligence is playing a major role in exacerbating the problems being blamed on large digital platforms such as a Facebook Inc. and Google LLC, according to a U.S. expert who testified before the International Grand Committee on Big Data, Privacy and Democracy meeting in Ottawa this week. Ben Scott, a Stanford fellow at the Centre for Internet and Society and a former advisor to Hillary Clinton, said that it wasn't until the tech giants got hooked on machine learning that concerns about their polarizing effect and impact on democracy really took off. "It's not just the ads that get targeted. The entire communications environment in which we live is now tailored by machine intelligence to hold our attention," he said. "The more time people spend on the platform, the more ads they see and the more money they make. It's a beautiful business model, and it works."