Alabama passed a 10 percent porn tax last year, as Utah and Pennsylvania eye similar bills. Half the country has enacted age-verification laws to prevent minors from viewing porn. As age-verification laws continue to dismantle the adult industry--and determine the future of free speech on the internet --a Utah lawmaker proposed a bill this week that would enforce a tax on porn sites that operate within the state. Introduced by state senator Calvin Musselman, a Republican, the bill would impose a 7 percent tax on total receipts "from sales, distributions, memberships, subscriptions, performances, and content amounting to material harmful to minors that is produced, sold, filmed, generated, or otherwise based" in Utah. If passed, the bill would go into effect in May and would also require adult sites to pay a $500 annual fee to the State Tax Commission.

RFK Jr.'s Health Department Is Pondering a National Men's Health Initiative At an FDA discussion of testosterone replacement therapy, a top official called for special health centers to address a "men's health crisis." Others called to ease men's access to hormones. The US Department of Health and Human Services is considering launching a federal men's health initiative, a source at the agency tells WIRED. Brian Christine, who will be sworn in on December 12 as assistant secretary for health at HHS and head of the United States Public Health Service Commissioned Corps, called for such an effort Wednesday during a Food and Drug Administration panel on testosterone replacement therapy (TRT) for men. A spokesperson for HHS declined to comment.

The emergence of global health crises, such as COVID-19 and Monkeypox (mpox), has underscored the importance of understanding public sentiment to inform effective public health strategies. This study conducts a comparative sentiment analysis of public perceptions surrounding COVID-19 and mpox by leveraging extensive datasets of 147,475 and 106,638 tweets, respectively. Advanced machine learning models, including Logistic Regression, Naive Bayes, RoBERTa, DistilRoBERTa and XLNet, were applied to perform sentiment classification, with results indicating key trends in public emotion and discourse. The analysis highlights significant differences in public sentiment driven by disease characteristics, media representation, and pandemic fatigue. Through the lens of sentiment polarity and thematic trends, this study offers valuable insights into tailoring public health messaging, mitigating misinformation, and fostering trust during concurrent health crises. The findings contribute to advancing sentiment analysis applications in public health informatics, setting the groundwork for enhanced real-time monitoring and multilingual analysis in future research.

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.

Data accuracy is essential for scientific research and policy development. The National Violent Death Reporting System (NVDRS) data is widely used for discovering the patterns and causes of death. Recent studies suggested the annotation inconsistencies within the NVDRS and the potential impact on erroneous suicide-cause attributions. We present an empirical Natural Language Processing (NLP) approach to detect annotation inconsistencies and adopt a cross-validation-like paradigm to identify problematic instances. We analyzed 267,804 suicide death incidents between 2003 and 2020 from the NVDRS. Our results showed that incorporating the target state's data into training the suicide-crisis classifier brought an increase of 5.4% to the F-1 score on the target state's test set and a decrease of 1.1% on other states' test set. To conclude, we demonstrated the annotation inconsistencies in NVDRS's death investigation notes, identified problematic instances, evaluated the effectiveness of correcting problematic instances, and eventually proposed an NLP improvement solution.

The contribution presents a study on figurative language of the first months of the COVID-19 crisis in Italian online newspapers. Particularly, we contrast topics and metaphorical language used by journalists in the first and second phase of the government response to the pandemic in Spring 2020. The analysis is conducted on a journalistic corpus collected between February 24th and June 3rd, 2020. The analysis is performed using both quantitative and qualitative approaches, combining Structural Topic Modelling (Roberts et al. 2016), Conceptual Metaphor Theory (Lakoff & Johnson, 1980), and qualitative-corpus based metaphor analysis (Charteris-Black, 2004). We find a significant shift in topics discussed across Phase 1 and Phase 2, and interesting overlaps in topic-specific metaphors. Using qualitative corpus analysis, we present a more in-depth case study discussing metaphorical collocations of the topics of Economy and Society

Join us on November 9 to learn how to successfully innovate and achieve efficiency by upskilling and scaling citizen developers at the Low-Code/No-Code Summit. From COVID--19 to monkeypox and intermittent polio scares, concerns around public health crises have significantly increased over the past several years. Living in a globally connected world amidst climate change and a growing population has enabled the emergence of more frequent viruses and fostered their spread. A research study last year estimated that the probability of novel disease outbreaks will grow three-fold in the next few decades. Fortunately, there have been significant technological developments that can help minimize the impact of these global health issues.

Michael Yu and colleagues examine the challenges in developing AI tools for use at point of care The covid-19 pandemic created unprecedented challenges for both clinicians and healthcare institutions. Adapting to a rapidly emerging disease while facing staff and material shortages prompted difficult decisions on how best to allocate resources. Artificial intelligence (AI) rapidly moved to the forefront of the effort to adapt our healthcare systems to coping with covid-19. Hundreds of new models were developed, promising best solutions for all aspects of patient care from diagnostics to therapeutics and logistics. Yet only a small minority of these models were deployed, and none became widely adopted.12 We argue that the covid-19 pandemic exposed flaws in the technological, institutional, and ethical foundations upon which AI must build to considerably improve bedside care. If AI is to be part of a rapid response to future health crises, the challenges that it faced during the covid-19 pandemic must be carefully analysed and overcome. AI is a branch of computer science that uses data and algorithms to extract meaning in a way that is characteristic of intelligent beings—that is, turning data into effective decision making processes. Research applications of AI in medicine have already emerged far and wide—for example, in drug discovery and modelling of complex biological systems. By contrast, efforts to integrate AI into everyday clinical care have had minimal success, despite the comparatively simple nature of the problems: optimising patient trajectories, maximising use of existing facilities, or determining when and how to reallocate resources. We surmise that this translational gap, which was magnified by the covid-19 pandemic, is due to the nature of the underlying data, the infrastructure through which they emerge, and the human context in which they occur. By understanding the influence of these factors on the chances …

The coronavirus pandemic has forced us to fundamentally rethink our relationship with technology. Remote work, remote learning and ehealth have allowed millions of people to continue their lives while social distancing to reduce the risk of spreading COVID. At the same time, this increased reliance on technology has shown just how much resources like voice technology and AI can influence society. We're now seeing technology like voice AI for emotional recognition being deployed at scale and just how impactful it can be. Carnegie Mellon researchers in Pittsburgh developed an app in 2020 that could detect signs of COVID-19 in someone's voice by analyzing the voice and breathing patterns of thousands of sick patients.

India and Pakistan have fought four wars in the past few decades, but when India faced an oxygen shortage in its hospitals during its recent COVID-19 surge, Pakistan offered to help. Finding these positive tweets, however, was not as easy as simply browsing the supportive hashtags or looking at the most popular posts. And Twitter's algorithm isn't tuned to surface the most positive tweets during a crisis. Ashique KhudaBukhsh of Carnegie Mellon University's Language Technologies Institute led a team of researchers who used machine learning to identify supportive tweets from Pakistan during India's COVID crisis. In the throes of a public health crisis, words of hope can be welcome medicine.