Correction

Objectives: During the early phase of the COVID-19 outbreak, misinformation spread rapidly, hindering effective health communication and fueling xenophobic violence. The politicization of health issues, along with the manipulation by social bots and astroturfing accounts, posed significant challenges. This study aims to investigate how misinformation spreads through social media, involving malicious actors like trolls and bots, and explores emotional contagion during public health crises. Methods: Using a computational methodology that combines semantic modeling, social network analysis, bot identification, emotion analysis, and time series analysis, the study analyzed over 700,000 tweets from February to July 2020. Results: The findings reveal that inauthentic actors amplified negative emotions, particularly among news and political actors, while positive emotions were less prominent. Astroturfing accounts acted as key nodes, perpetuating negative emotional contagion. Conclusion: This study provides a framework for monitoring emotional responses in public health crises, with findings applicable beyond COVID-19 to other public health emergencies.

Mass media plays an important role during a public health crisis by disseminating information, news and spreading awareness among the masses about combating a health emergency. In the context of the Covid-19 pandemic impacting lives across the globe since its advent in December 2019, this study investigates the role played by TV news channels in India through their unstinted coverage of the same. The study focused on two objectives – 1) finding out viewership trends and consumption patterns among television news audiences in Gujarat during the lockdown and 2) studying the nature of Covid coverage found on mainstream private television news channels in India. A survey with 160 television news viewers was conducted in Ahmedabad and Vadodara. Results demonstrate the reach, significance and impact of television news in a country like India, especially during a health emergency situation. Despite the flourishing of digital content and the rise in internet penetration, television remains a trusted and dependable source of information and knowledge seeking for Indian audiences. In light of the role played by television during the Covid crisis, the future shall witness more scope and opportunities for television to influence viewers with respect to health-related behaviour and practices, development issues and larger global concerns.

BackgroundDuring global health crises such as the COVID-19 pandemic, rapid spread of misinformation on social media has occurred. The misinformation associated with COVID-19 has been analyzed, but little attention has been paid to developing a comprehensive analytical framework to study its spread on social media.ObjectiveWe propose an elaboration likelihood model–based theoretical model to understand the persuasion process of COVID-19–related misinformation on social media.MethodsThe proposed model incorporates the central route feature (content feature) and peripheral features (including creator authority, social proof, and emotion). The central-level COVID-19–related misinformation feature includes five topics: medical information, social issues and people’s livelihoods, government response, epidemic spread, and international issues. First, we created a data set of COVID-19 pandemic–related misinformation based on fact-checking sources and a data set of posts that contained this misinformation on real-world social media. Based on the collected posts, we analyzed the dissemination patterns.ResultsOur data set included 11,450 misinformation posts, with medical misinformation as the largest category (n=5359, 46.80%). Moreover, the results suggest that both the least (4660/11,301, 41.24%) and most (2320/11,301, 20.53%) active users are prone to sharing misinformation. Further, posts related to international topics that have the greatest chance of producing a profound and lasting impact on social media exhibited the highest distribution depth (maximum depth=14) and width (maximum width=2355). Additionally, 97.00% (2364/2437) of the spread was characterized by radiation dissemination.ConclusionsOur proposed model and findings could help to combat the spread of misinformation by detecting suspicious users and identifying propagation characteristics.

This study investigates the temporal dynamics and causal relationships among risk-focused, prevention-focused, and mixed (risk and prevention) media messages during the early stage of the COVID-19 outbreak in South Korea, from December 31, 2019 to February 2020. Although media coverage plays a pivotal role in shaping public perception and encouraging preventive behavior during health crises, there is limited empirical research on how different types of messages evolve and interact over time. The news data used in this study were collected from BigKinds, a platform operated by the Korea Press Foundation, and include articles from major national daily newspapers, economic newspapers, broadcasters, specialized media, and online news outlets, as well as transcribed texts of some TV news programs. Using a corpus of Korean news articles, we categorized each article into one of the three message types and applied a series of time series analyses including cross-correlation, Granger causality tests, vector autoregression (VAR), cointegration tests, and vector error correction modeling (VECM). The results show a sequential dynamic in which risk-focused reporting significantly precedes mixed messaging, which subsequently leads to an increase in prevention-focused articles, typically with a lag of 1 to 2 weeks. Impulse response functions from the VAR model support this cascading pattern, while VECM results confirm the presence of a long-run equilibrium and short-run adjustment mechanisms among the three categories. This study offers three key contributions. First, it theorizes mixed messaging as a transitional stage between risk and prevention frames, addressing limitations in binary message models. Second, it provides an empirical framework for analyzing time-lagged media dynamics based on actual news content. Third, it reveals that prevention messaging was delayed during the early stages of the pandemic, suggesting a need for more timely and integrated communication strategies in future public health emergencies.

Guided by the Influence of Presumed Media Influence model, this study investigated how exposure to health misinformation shapes public support for regulating such misinformation on social media in the context of COVID-19. An online survey was conducted in South Korea (N = 400), and the data were analyzed using a moderated mediation model with the SPSS PROCESS macro. Results revealed that exposure to COVID-19 misinformation significantly increased perceptions of its influence on others. However, perceived influence on others alone did not predict support for regulation. Instead, the indirect effect of exposure on regulatory support-via presumed influence on others-emerged only when individuals also perceived misinformation as personally affecting themselves. These findings underscore that public support for regulating health misinformation is strongest when misinformation is viewed as a shared health threat, endangering both individuals and the broader community. This suggests that in the context of public health crises, regulatory attitudes are driven not solely by concern for others but by the combined recognition of personal and collective risks. The study offers theoretical insights and practical implications for policymakers, health communicators, and social media platforms seeking to design policies or interventions that protect public health by countering misinformation.

Judge Steven Leifman, who began the Miami‐Dade County Criminal Mental Health Project, is the focus of a new documentary on PBS, The Definition of Insanity, TV News Desk reported April 9. The documentary explores the groundbreaking work of the Miami‐Dade Criminal Mental Health Project (CMHP), an approach being heralded as a model for helping to solve the mental health crisis in America. A people‐powered communitywide safety net, the CMHP works through the court system to steer people with mental illness — as their legal cases hang in the balance — on a path from incarceration to recovery. Shocked by how people with mental illness were treated in Miami‐Dade's jails, Leifman works with a team of dedicated public servants, as well as former adversaries in the criminal justice system, to help people with mental illness navigate from lives of tragedy to possibility. Building on the story of Miami's once‐failed mental health system — a familiar national narrative of over‐incarceration and brutal mistreatment — The Definition of Insanity instead reveals a humane criminal justice approach to mental illness that is orchestrated from the court outward into the community. The Definition of Insanity premiered on PBS April 14.