Embodying Temporality in Response Efficacy: How Coping Appraisal Promotes Preventive Behavioral Engagement During the Ongoing Pandemic.

This study heightens the understanding of response efficacy as a determinant of engagement in preventive behavior during the ongoing COVID-19 pandemic. To achieve a more nuanced conceptualization of response efficacy, this study proposes and examines a time-based coping appraisal framework, drawing upon a part of the extended parallel process model (EPPM). The current study investigates how the temporality of coping response message features work in generating social distancing behavioral intentions and explicates the role of anticipated emotions in this motivational process. Results of the experimental study (N = 584) indicate that proximal future framed coping response message (vs. distant future message) led to greater response efficacy, which in turn led to greater anticipated guilt and pride, and by extension increased social distancing behavioral intentions. Mediation analyses also demonstrated the indirect effects of the temporality conditions (proximal future-oriented message vs. distant future-oriented message) on social distancing intentions through response efficacy, anticipated guilt, and anticipated pride. The theoretical and practical implications of this study are discussed.

Combating COVID-19 Vaccine Hesitancy: A Synthetic Public Segmentation Approach for Predicting Vaccine Acceptance.

OBJECTIVE: Vaccine hesitancy impacts the ability to cope with coronavirus disease 2019 (COVID-19) effectively in the United States. It is important for health organizations to increase vaccine acceptance. Addressing this issue, this study aimed to predict citizens' acceptance of the COVID-19 vaccine through a synthetic approach of public segmentation including cross-situational and situational variables. Controlling for demographics, we examined institutional trust, negative attitudes toward, and low levels of knowledge about vaccines (ie, lacuna public characteristics), and fear of COVID-19 during the pandemic. Our study provides a useful framework for public segmentation and contributes to risk and health campaigns by identifying significant predictors of COVID-19 vaccine acceptance. METHOD: We conducted an online survey on October 10, 2020 (N = 499), and performed hierarchical regression analyses to predict citizens' COVID-19 vaccine acceptance. RESULTS: This study demonstrated that trust in the Centers for Disease Control and Prevention (CDC) and federal government, vaccine attitude, problem recognition, constraint recognition, involvement recognition, and fear positively predicted COVID-19 vaccine acceptance. CONCLUSIONS: This study outlines a useful synthetic public segmentation framework and extends the concept of lacuna public to the pandemic context, helping to predict vaccine acceptance. Importantly, the findings could be useful in designing health campaign messages.

Dealing with the COVID-19 crisis: Theoretical application of social media analytics in government crisis management.

Little theory-grounded research addresses how to use social media strategically in government public relations through machine learning. To fill this gap, we propose a way to optimize social media analytics to manage issues and crises by using the framework of attribution theory to analyze 360,861 tweets. In particular, we examined the attribution of crisis responsibility related to the spread of COVID-19 and its relations to the negative emotions of U.S. citizens on Twitter for six months (from January 20 to June 30, 2020). The results of this study showed that social media analytics is a valid tool to monitor how the spread of COVID-19 evolved from an issue to a crisis for the Trump administration. In addition, the federal government's lack of response and inability to handle the outbreak led to citizens' engagement and amplification of negative tweets that blamed the Trump White House. Theoretical and practical implications of the results are discussed.

Coping with mental health issues via communicative action in the digital age: testing cybercoping models.

BACKGROUND: Mental health issues have become critical social problems around the world. Individuals who are suffering from mental health problems tend to obtain health information and social support in the digital media environment. However, there has been little research on how information-seeking and information-forwarding behaviors affect sufferers' coping processes and outcomes. Based on communicative behaviors of people in online to solve health problmes, this study aims to apply two cybercoping modes proposed by previous reserach to mental health issues. METHOD: Using anxiety and depression issues, this study conducted an online survey (N = 560) to examine two cybercoping models with data collected from an online crowdsourcing platform (Amazon's Mechanical Turk). Structural equation modeling (SEM) was conducted to test the cybercoping models in the context of mental health issues. RESULTS: Results of the first cybercoping model showed that information seeking was positively associated with coping outcomes, whereas information forwarding was not significant. While information seeking in the second cybercoping model was significantly associated with other factors, information forwarding was not significant. The second model revealed that coping processes fully mediate between information seeking and cybercoping outcomes. CONCLUSIONS: This study contributes to the theoretical building of cybercoping models in the context of mental health issues by illustrating the effects of information seeking on cybercoping outcomes. Health care practitioners may use these results to better understand which communicative processes in online mental health communication are positively associated with specific coping processes and outcomes.

From Mind to Mouth: Understanding Active Publics in China and Their Communicative Behaviors on GM Foods.

Using an online survey conducted in China (N = 1089), this study aims to understand the characteristics of active publics on the issue of genetically modified (GM) foods and provide effective communication strategies with active publics in China. In doing so, this study segments active publics regarding GM foods and predicts their communicative behaviors on GM foods by using the theoretical framework of situational theory of problem solving (STOPS). The results of the study revealed the demographic characteristics of active publics, situational, and media factors to predict information seeking, forefending, and forwarding about GM foods. Theoretical and practical implications of the findings are discussed.

Are Chinese Netizens Willing to Speak Out? The Spiral of Silence in Public Reactions to Controversial Food Safety Issues on Social Media.

This study examines the influential factors posited by the Spiral of Silence Theory (SoS) in shaping people's perceptions of the overall public opinion towards food safety issues in China and their willingness to speak out. Two highly controversial issues, including genetically modified (GM) food and food additives, are examined. Using an online opt-in panel in China, we collected survey responses from a total of 1089 respondents, with a comparable age distribution to that of Chinese netizens, as indicated in the most recent census. Ordinary Least Squares (OLS) regressions were conducted to make statistical inferences about the proposed research questions and hypotheses. Findings suggest that perceived opinion incongruence, self-relevance, and self-influence significantly affected the extent to which people were willing to express their opinions on social media for the genetically modified food issue, but not the use of food additive issue. The study provides evidence of the silencing effect on publicly expressing opinions about the food safety related issues in China and clarifies the potential boundary conditions of the SoS mechanism in the context of Chinese social media where the majority of public opinions come into formation.

Predicting Public Support for Government Actions in a Public Health Crisis: Testing Fear, Organization-Public Relationship, and Behavioral Intention in the Framework of the Situational Theory of Problem Solving.

This study aimed to examine a theoretical model to predict publics' communicative actions to take and transmit information from the Centers for Disease Control and Prevention (CDC), as well as their behavioral intentions to follow the CDC's instructions in the context of infectious disease outbreaks. Using the framework of the situational theory of problem-solving (STOPS), this study tested the roles of fear and positive organization-public relationship (OPR), linking STOPS and behavioral intentions to follow CDC instructions. The results of this study suggest that fear and positive OPR variables play vital roles in predicting individuals' information acquisition, information transmission, and willingness to follow the CDC's instructions to cope with infectious diseases.

Who Is Caring for the Caregiver? The Role of Cybercoping for Dementia Caregivers.

The purpose of this study is to investigate the relationship between dementia caregivers' communication behaviors (information seeking and forwarding) and their outcomes (coping outcomes: e.g., dealing better with negative feelings or improved medical outcomes). A survey data set of dementia patients' caregivers substantiates the effects of communication behaviors about dementia illness on coping outcomes, as well as the mediating role of emotion-focused and problem-focused coping processes. Using structural equation modeling (SEM), this study found positive effects of communication behaviors on outcomes through coping processes. Further, the results indicate that communication behaviors in cyberspace are crucial for caregivers to cope with dementia, both affectively (improvement of caregivers' emotional control) and physically (health improvement of patients). The implications for the improvement of public health through online health communication behaviors are discussed.

One does not fit all: Health audience segmentation and prediction of health behaviors in cancer prevention.

The purpose of this study is to propose a health belief model-based approach to segmenting health audiences in order to improve targeting of cancer prevention efforts. This study also examines how the identified health segments differ in cancer prevention behaviors. Using data from the 2013 Health Information National Trends Survey, a cluster analysis resulted in three distinct health audience groups: (a) health aware, (b) health at risk, and (c) health in confidence. MANOVA tests indicate that these segments differ significantly regarding healthy diet and exercise. The findings inform cancer prevention campaigns targeting priority audiences.

Tweeting as Health Communication: Health Organizations' Use of Twitter for Health Promotion and Public Engagement.

This study examined how major health organizations use Twitter for disseminating health information, building relationships, and encouraging actions to improve health. The sampled organizations were the American Heart Association, American Cancer Society, and American Diabetes Association. A content analysis was conducted on 1,583 tweets to examine these organizations' use of Twitter's interactive features and to understand the message functions and topics of their tweets. The numbers of retweets and favorites were also measured as engagement indicators and compared by different message functions. The results revealed that all of the organizations posted original tweets most, but they differed in the degree to which they used the retweet and reply functions. Hashtags and hyperlinks were the most frequently used interactive tools. The majority of the tweets were about organization-related topics, whereas personal health-related tweets represented a relatively small portion of the sample. Followers were most likely to like and retweet personal health action-based messages.