Objective Facial masks are an essential personal protective measure to fight the COVID-19 pandemic. However, the mask adoption rate in the US is still less than optimal. This study aims to understand the beliefs held by individuals who oppose the use of facial masks, and the evidence that they use to support these beliefs, to inform the development of targeted public health communication strategies. Materials and Methods We analyzed a total of 771,268 US-based tweets between January to October 2020. We developed machine-learning classifiers to identify and categorize relevant tweets, followed by a qualitative content analysis of a subset of the tweets to understand the rationale of those opposed mask wearing. Results We identified 267,152 tweets that contained personal opinions about wearing facial masks to prevent the spread of COVID-19. While the majority of the tweets supported mask wearing, the proportion of anti-mask tweets stayed constant at about 10% level throughout the study period. Common reasons for opposition included physical discomfort and negative effects, lack of effectiveness, and being unnecessary or inappropriate for certain people or under certain circumstances. The opposing tweets were significantly less likely to cite external sources of information such as public health agencies’ websites to support the arguments. Discussion and Conclusion Combining machine learning and qualitative content analysis is an effective strategy for identifying public attitudes toward mask wearing and the reasons for opposition. The results may inform better communication strategies to improve the public perception of wearing masks and, in particular, to specifically address common anti-mask beliefs.

Effectively engaging citizens during crises is critical for governments to disseminate timely information and help the public to adjust to the constantly changing conditions. In particular, promoting youth engagement not only enhances crisis awareness and resilience among the young generation, but also has a positive impact on youths' social participation and responsibility. With the increasing popularity of online video services, leveraging online videos to disseminate authoritative information has become a method widely adopted by government. To enhance youth awareness and engagement, two new video-based crisis communication strategies have been utilized on a popular youth-targeted video platform Bilibili in China: creating recreational videos such as animation and music videos, and collaborating with individual video-uploaders in video making. However, their impacts and results are largely unknown, which motivates our study. Guided by Entertainment Education (EE) and Collaborative Governance (CG), we report, to our best knowledge, the first systematic study on how recreational video category and government-citizen collaboration would influence youth engagement focusing on 3347 COVID-19-related government-generated videos on Bilibili. This study reveals that recreational videos successfully promote youth engagement including interaction, feedback and sharing. Collaboration with individual uploaders in video making also has a substantially positive impact on youth engagement. Through an in-depth qualitative content analysis of user-generated commentaries, we further unpacked the unique values (e.g., trust work for youth participation) as well as latent limitations (e.g., imbalanced topic distribution) of the two new strategies. We discuss how the findings enrich EE and CG theoretically, and provide practical implications to effective and engaging communication strategies during crises.

Online videos are playing an increasingly important role in timely information dissemination especially during public crises. Video commentary, synchronous or asynchronous, is indispensable in viewers' engagement and participation, and may in turn contribute to video with additional information and emotions. Yet, the roles of video commentary in crisis communications are largely unexplored, which we believe that an investigation not only provides timely feedback but also offers concrete guidelines for better information dissemination. In this work, we study two distinct commentary features of online videos: traditional asynchronous comments and emerging synchronous danmaku. We investigate how users utilize these two features to express their emotions and share information during a public health crisis. Through qualitative analysis and applying machine learning techniques on a large-scale danmaku and comment dataset of Chinese COVID-19-related videos, we uncover the distinctive roles of danmaku and comments in crisis communication, and propose comprehensive taxonomies for information themes and emotion categories of commentary. We also discover the unique patterns of crisis communications presented by danmaku, such as collective emotional resonance and style-based highlighting for emphasizing critical information. Our study captures the unique values and salient features of the emerging commentary interfaces, in particular danmaku, in the context of crisis videos, and further provides several design implications to enable more effective communications through online videos to engage and empower users during crises.