The popularity of contradictory information about COVID-19 vaccine on social media in China

Heuristic Information Processing as a Mediating Factor in the Process of Exposure to COVID-19 Vaccine Information and Misinformation Sharing on Social Media

Social media use for risk communication during the COVID-19 pandemic has caused considerable concerns about an overabundance of information, particularly misinformation. However, how exposure to COVID-19 information on social media can lead to subsequent misinformation sharing during the pandemic has received little research attention. This study adopted the social amplification of risk framework to delineate how exposure to COVID-19 vaccine information on social media can be associated with individuals' misinformation sharing through heuristic information processing. The role of social media trust was also examined. Results from an online survey (N = 1488) of Chinese Internet users revealed that exposure to COVID-19 vaccine information on social media was associated with misinformation sharing, mediated by both affect heuristics (i.e., negative affect toward the COVID-19 pandemic in general) and availability heuristics (i.e., perceived misinformation availability). Importantly, both high and low levels of trust in social media strengthened the mediating associations. While a low level of trust strengthened the association between exposure to COVID-19 vaccine information on social media and the affect heuristics, a high level of trust strengthened its association with the availability heuristics, both of which were associated with misinformation sharing. Our findings suggest that heuristic information processing is essential in amplifying the spread of misinformation after exposure to risk information on social media. It is also suggested that individuals should maintain a middle level of trust in social media, being open while critical of risk information on social media.

Sustaining positive perceptions of science in the face of conflicting health information: An experimental test of messages about the process of scientific discovery

BACKGROUND

The public is often exposed to conflicting health information, with evidence of concerning consequences, yet little attention has been paid to identifying strategies that can mitigate its effects.

OBJECTIVE

The current study tests whether three different approaches to communicating about the process of scientific discovery-a rational appeal using analogical evidence, a rational appeal using testimonial evidence, and a logic-based inoculation approach-could reduce the adverse effects of exposure to conflict by positively framing how people construe the scientific process, increasing their perceived knowledge about the scientific process, and helping them to respond to critiques about the scientific process, which, in turn, might make them less apt to counterargue the science they subsequently encounter in health news stories and other exposures to conflict.

METHODS

We fielded a survey experiment in May 2022 with a national sample of U.S. adults (N = 1604).

RESULTS

Providing any of the three messages about science prior to exposure to conflicting health information encouraged both positive construal of science and greater science knowledge perceptions and discouraged counterarguing science, compared to a control condition in which people were only exposed to conflict. Of the three messaging approaches tested, the testimonial evidence message was slightly more effective, but was also considered slightly more accurate, credible, and trustworthy.

CONCLUSIONS

Developing and implementing messages that describe the process of scientific discovery could prove successful, not only in improving public perceptions of science but perhaps ultimately in better equipping people to make sense of conflicting information and its causes. However, additional research on such strategies is needed, particularly as part of larger interventions with multiple messages across multiple exposures, if they are to have implications for health and science communication.

Analysis of individual characteristics influencing user polarization in COVID-19 vaccine hesitancy

During the COVID-19 pandemic, vaccine hesitancy proved to be a major obstacle in efforts to control and mitigate the negative consequences of COVID-19. This study centered on the degree of polarization on social media about vaccine use and contributing factors to vaccine hesitancy among social media users. Examining the discussion about COVID-19 vaccine on the Weibo platform, a relatively comprehensive system of user features was constructed based on psychological theories and models such as the curiosity-drive theory and the big five model of personality. Then machine learning methods were used to explore the paramount impacting factors that led users into polarization. Findings revealed that factors reflecting the activity and effectiveness of social media use promoted user polarization. In contrast, features reflecting users' information processing ability and personal qualities had a negative impact on polarization. This study hopes to help healthcare organizations and governments understand and curb social media polarization around vaccine development in the face of future surges of pandemics.

Dynamic mechanism of eliminating COVID-19 vaccine hesitancy through web search

This research focuses on the research problem of eliminating COVID-19 vaccine hesitancy through web search. A dynamic model of eliminating COVID-19 vaccine hesitancy through web search is constructed based on the Logistic model, the elimination degree is quantified, the elimination function is defined to analyze the dynamic elimination effect, and the model parameter estimation method is proposed. The numerical solution, process parameters, initial value parameters and stationary point parameters of the model are simulated, respectively, and the mechanism of elimination is deeply analyzed to determine the key time period. Based on the real data of web search and COVID-19 vaccination, data modeling is carried out from two aspects: full sample and segmented sample, and the rationality of the model is verified. On this basis, the model is used to carry out dynamic prediction and verified to have certain medium-term prediction ability. Through this research, the methods of eliminating vaccine hesitancy are enriched, and a new practical idea is provided for eliminating vaccine hesitancy. It also provides a method to predict the quantity of COVID-19 vaccination, provides theoretical guidance for dynamically adjusting the public health policy of the COVID-19, and can provide reference for the vaccination of other vaccines.

Extracting Useful Emergency Information from Social Media: A Method Integrating Machine Learning and Rule-Based Classification

User-generated contents (UGCs) on social media are a valuable source of emergency information (EI) that can facilitate emergency responses. However, the tremendous amount and heterogeneous quality of social media UGCs make it difficult to extract truly useful EI, especially using pure machine learning methods. Hence, this study proposes a machine learning and rule-based integration method (MRIM) and evaluates its EI classification performance and determinants. Through comparative experiments on microblog data about the "July 20 heavy rainstorm in Zhengzhou" posted on China's largest social media platform, we find that the MRIM performs better than pure machine learning methods and pure rule-based methods, and that its performance is influenced by microblog characteristics such as the number of words, exact address and contact information, and users' attention. This study demonstrates the feasibility of integrating machine learning and rule-based methods to mine the text of social media UGCs and provides actionable suggestions for emergency information management practitioners.

Information avoidance in the age of COVID-19: A meta-analysis

Guided by three major theoretical frameworks, this meta-analysis synthesizes 17 empirical studies (15 articles with 18,297 participants, 13 of them are from non-representative samples) and quantifies the effect sizes of a list of antecedents (e.g., cognitive, affective, and social factors) on information avoidance during the COVID-19 context. Findings indicated that information-related factors including channel belief (r = -0.35, p < .01) and information overload (r = 0.23, p < .01) are more important in determining individual's avoidance behaviors toward COVID-19 information. Factors from the psychosocial aspects, however, had low correlations with information avoidance. While informational subjective norms released a negative correlation (r = -0.16, p < .1) which was approaching significant, positive and negative risk responses were not associated with information avoidance. Moderator analysis further revealed that the impacts of several antecedents varied for people with different demographic characteristics (i.e., age, gender, region of origin), and under certain sampling methods. Theoretically, this meta-analysis may help determine the most dominant factors from a larger landscape, thus providing valuable directions to refine frameworks and approaches in health information behaviors. Findings from moderator analysis have also practically inspired certain audience segmentation strategies to tackle occurrence of information avoidance during the COVID-19 pandemic.

Spatial-Temporal Pattern Evolution of Public Sentiment Responses to the COVID-19 Pandemic in Small Cities of China: A Case Study Based on Social Media Data Analysis

The impact of the COVID-19 pandemic on public mental health has become increasingly prominent. Therefore, it is of great value to study the spatial-temporal characteristics of public sentiment responses to COVID-19 exposure to improve urban anti-pandemic decision-making and public health resilience. However, the majority of recent studies have focused on the macro scale or large cities, and there is a relative lack of adequate research on the small-city scale in China. To address this lack of research, we conducted a case study of Shaoxing city, proposed a spatial-based pandemic-cognition-sentiment (PCS) conceptual model, and collected microblog check-in data and information on the spatial-temporal trajectory of cases before and after a wave of the COVID-19 pandemic. The natural language algorithm of dictionary-based sentiment analysis (DSA) was used to calculate public sentiment strength. Additionally, local Moran's I, kernel-density analysis, Getis-Ord Gi* and standard deviation ellipse methods were applied to analyze the nonlinear evolution and clustering characteristics of public sentiment spatial-temporal patterns at the small-city scale concerning the pandemic. The results reveal that (1) the characteristics of pandemic spread show contagion diffusion at the micro level and hierarchical diffusion at the macro level, (2) the pandemic has a depressive effect on public sentiment in the center of the outbreak, and (3) the pandemic has a nonlinear gradient negative impact on mood in the surrounding areas. These findings could help propose targeted pandemic prevention policies applying spatial intervention to improve residents' mental health resilience in response to future pandemics.