Legacy and social media respectively influence risk perceptions and protective behaviors during emerging health threats: A multi-wave analysis of communications on Zika virus cases.

ObjectiveBoth legacy media, such as television and newspapers, and online social media are potentially important but incompletely understood sources of information in the face of emerging public health risks. This research aimed to understand media effects on risk perceptions and behaviors concerning the Zika virus in the United States.MethodsWe analyzed a multi-wave nationally representative survey (N = 29,062) and the volume of communications in social and legacy media (i.e., legacy media data from news sources and databases, N = 2,660 and social media data from Twitter, N = 1,605,752) in the United States between April and October 2016, dates coinciding with the early cases of local transmission of Zika in the United States (i.e., 25 weeks). The present study conducted econometric analyses (i.e., Granger causality tests) to assess the associations of legacy and social media coverage with risk perceptions and protective behaviors in the total sample and specific groups separated by pregnancy status/intent, geographic region, income, education level, age, and ethnicity.ResultsThe results from the overall sample suggested that changes in the volume of information in legacy and social media (i.e., Twitter) were followed by different changes in community risk perceptions and protective behaviors. Specifically, social media coverage correlated with the level of risk perceptions, whereas the legacy media coverage correlated with the level of protective behaviors. Analyses across different subpopulations, including those of different pregnancy status/intent, geographic Zika risk, income, education level, age, and ethnicity, replicated the social media associations with risk perceptions in most cases. However, legacy media and protective behaviors were linked only in some vulnerable subpopulations (e.g., the less-educated populations).ConclusionUnderstanding how media coverage relates to Zika risk perceptions and protective behaviors will help to facilitate effective risk communications by healthcare professionals and providers, particularly when a health risk emerges

Meta-Analysis of Misinformation, Debunking, and Misinformation-Persistence

Individuals who download the dataset agree to not publish (in print, on television, on radio, or via the Internet) until that time the information contained in the dataset is published by Psychological Scienc

Correction Effects in Science-Relevant Misinformation

A meta-analysis project of science-related misconception and misinformatio

Toward a relativistic approach to social support

People depend heavily on various forms of assistance, guidance, and care for survival, which leads many to view social support as definitively beneficial. However, recent studies have provided the counterargument that social support is not necessarily a panacea for coping with stress. A considerable number of studies have been conducted on social support, yet the majority of the theoretical models developed to understand its influence have focused on its benefits, with few exploring the negative support effects from the relational aspect. No studies have attempted to explain support effects from the perspective of individual differences. More importantly, the underlying social support mechanism and the roles played by the different modes of social support remain unknown. Building on the available theoretical insights, a relativistic approach is adopted here to study social support. A hybrid self-focus model of social support is proposed to understand the relationships between personality resources (specifically self-esteem) and levels of affect (positive and negative), the underlying mechanism of self-focus processes (public self-consciousness and social comparison), and the moderating role of the mode of social support (offline and online). The two studies incorporated correlational and quasi-experimental methodologies conducted to examine the proposed model among participants from diverse socioeconomic backgrounds. Study 1 was correlational and applied a naturalistic categorization method to the mode of social support. This study showed that the focus of social comparison varied based on the levels of self-esteem and engagement in the offline and mixed modes of social support, but the findings were inconclusive regarding social comparison as the underlying mechanism. In addition, the analyses of public self-consciousness revealed puzzling results. Hence, the findings only provided partial support for the hybrid self-focus model of social support. To clarify the counterintuitive findings revealed in Study 1, Study 2 adopted a quasi-experimental design to examine the mediating effects of self-focus processes on the relationship between self-esteem and levels of affect in two distinct modes of social support. One hundred and seventy-seven participants were included in the moderated mediation analyses, and the findings were largely consistent with the proposed model of public self-consciousness as the facilitating mechanism. People’s awareness of the self-referent aspects that were matters for public display explained the positive link between self-esteem and distress. Such a positive indirect effect of self-esteem mediated through public self-consciousness was particularly strong in offline social support. In summary, the present project demonstrates that support effects are influenced by self-esteem, public self-consciousness, and the mode of social support. These findings provide unique insights that have not been examined by previous studies on social support. This project is the first attempt to address knowledge gaps by adopting a relativistic approach to social support. The subsequent discussion, implications, and future directions focus on a relativistic approach and the hybrid self-focus model of social support.published_or_final_versionPsychologyDoctoralDoctor of Philosoph

Predicting Opioid Overdose Crude Rates with Text-Based Twitter Features (Student Abstract)

Drug use reporting is often a bottleneck for modern public health surveillance; social media data provides a real-time signal which allows for tracking and monitoring opioid overdoses. In this work we focus on text-based feature construction for the prediction task of opioid overdose rates at the county level. More specifically, using a Twitter dataset with over 3.4 billion tweets, we explore semantic features, such as topic features, to show that social media could be a good indicator for forecasting opioid overdose crude rates in public health monitoring systems. Specifically, combining topic and TF-IDF features in conjunction with demographic features can predict opioid overdose rates at the county level

The effects of scientific messages and narratives about vaccination.

A fundamental challenge complicates news decisions about covering vaccine side effects: although serious vaccine side effects are rare, less severe ones do occur occasionally. The study was designed to test whether a side effect message could induce vaccine hesitancy and whether that could be countered by pro-vaccine messages about vaccine safety. A large (N = 2,345), nationally representative experiment was conducted by randomly exposing participants to one of six videos about the measles, mumps, and rubella (MMR) vaccine edited from news programs produced during the 2019 measles outbreak in the United States. The design was a 2x3 factorial crossing the presence or absence of a hesitancy-inducing narrative message with a pro-vaccine science-supporting message (i.e., no message, science-supporting expert message, or pro-vaccine narrative message), leading to a total of six conditions. A general linear model was used to assess the effects of these videos on respondents' (1) vaccine risk perceptions, (2) policy views on vaccination, (3) willingness to encourage others to vaccinate their children, and (4) intention to send a pro-vaccine letter to their state representative. Findings indicated that the science-supporting expert message about vaccine safety led to higher pro-vaccine evaluations relative to other conditions [e.g., b = -0.17, p < .001, a reduction in vaccine risk perceptions of 0.17 as compared to the control]. There was also suggestive evidence that the hesitancy-inducing narrative may limit the effectiveness of a science-supporting expert message, although this finding was not consistent across different outcomes. When shown alone the hesitancy-inducing narrative did not shift views and intentions, but more research is needed to ascertain whether exposure to such messages can undercut the pro-vaccine influence of science-supporting (expert) ones. All in all, however, it is clear that science-supporting messages are effective and therefore worthwhile in combating vaccine misinformation