The Dynamic Effects of Perceptions of Dread Risk and Unknown Risk on SNS Sharing Behavior During Emerging Infectious Disease Events: Do Crisis Stages Matter?

In response to the increasing prevalence of emerging infectious disease (EID) threats, individuals are turning to social media platforms to share relevant information in ever greater numbers. In this study, we examine whether risk perceptions related to user-generated content have dynamic impacts on social networking site (SNS) sharing behavior in different crisis stages. To answer this question, we applied psychometric analysis to evaluate how dread risk and unknown risk can characterize EID threats. Drawing broadly on the literature of risk perceptions, self-perception theory, and crisis stages, we relied on microblogs collected from Sina Weibo, utilizing the vector autoregression model to analyze dynamic relationships. We found that perceptions of dread risk have a dominant and immediate impact on SNS sharing behavior in the buildup, breakout, and termination stages of EID events. Perceptions of unknown risk have a dominant and persistent impact on sharing behavior in the abatement stage. The joint effect of these two types of risk perception reveal an antagonism impact on SNS sharing behavior, and perceptions of dread- and unknown risk have interaction effects from the buildup to termination stages of EID events. To check robustness, we analyzed keywords related to perceptions of dread- and unknown risk. The results of this study support the empirical application of Slovic’s risk perception framework for understanding the characteristics of EID threats and provide a picture of how perceptions of dread- and unknown risk exert differential time-varying effects on SNS sharing behavior during EID events. We also discuss theoretical and practical implications for the crisis management of EID threats. This study is among the first that uses user-generated content in social media to investigate dynamic risk perceptions and their relationship to SNS sharing behavior, which may help provide a basis for timely and efficient risk communication

When Infodemic Meets Epidemic: a Systematic Literature Review

Epidemics and outbreaks present arduous challenges requiring both individual and communal efforts. Social media offer significant amounts of data that can be leveraged for bio-surveillance. They also provide a platform to quickly and efficiently reach a sizeable percentage of the population, hence their potential impact on various aspects of epidemic mitigation. The general objective of this systematic literature review is to provide a methodical overview of the integration of social media in different epidemic-related contexts. Three research questions were conceptualized for this review, resulting in over 10000 publications collected in the first PRISMA stage, 129 of which were selected for inclusion. A thematic method-oriented synthesis was undertaken and identified 5 main themes related to social media enabled epidemic surveillance, misinformation management, and mental health. Findings uncover a need for more robust applications of the lessons learned from epidemic post-mortem documentation. A vast gap exists between retrospective analysis of epidemic management and result integration in prospective studies. Harnessing the full potential of social media in epidemic related tasks requires streamlining the results of epidemic forecasting, public opinion understanding and misinformation propagation, all while keeping abreast of potential mental health implications. Pro-active prevention has thus become vital for epidemic curtailment and containment

SoMeIL: A social media infodemic listening for public health behaviours conceptual framework

Introduction The coronavirus disease 2019 (COVID-19) pandemic has escalated health infodemics given substantially digitalized daily life since the pandemic began. The number of social media users has skyrocketed. However, this has brought issues given misleading health information circulating on social media platforms that can lead to undesirable behaviours compromising individual or public health in real life. One long-lasting health issue is vaccine hesitancy, which has been further compounded by health infodemics on social media. According to the World Health Organization, health infodemics occur when too much information that makes true information competes with misinformation for people’s attention, understanding, and adherence to recommended health interventions. Existing theories and theoretical constructs have been applied to study public behaviours influenced by health infodemics on social media. However, these theories have limited to individual behaviours and ignored other critical factors. Furthermore, the current theories have rarely reflected the nature of social media as information can be disseminated instantly and massively without geographical restrictions regardless of information quality. Therefore, this dissertation aimed to address these limitations by proposing a solution that can listen to public discourse on social media and infer their behavioural intentions in real life. Methods The scoping review (Study I) was conducted by following the methods of Arksey and O'Malley as well as Levac et al. to identify and synthesize literature related to the research question. The theory construction methodology was used in the conceptual paper (Study II) to review existing theories and propose a new conceptual framework. Next, the Latent Dirichlet allocation topic modelling and qualitative thematic analysis were applied in the preliminary and partial qualitative validation study (Study III). The last study (Study IV) applied structural equation modeling (SEM) to infer people’s intentions toward COVID-19 vaccination in real life from Twitter amid the pandemic as a preliminary and partial validation for the proposed conceptual framework. Results A total of 2,405 articles published between November 1, 2019, and November 4, 2020, were retrieved from PubMed, Scopus, and PsycINFO. After removing duplicates, non-empirical literature, and irrelevant studies, a total of 81 articles written in English published in peer-reviewed journals were included in the scoping review (Study I). Six themes were found and reported: (1) surveying public attitudes, (2) identifying infodemics, (3) assessing mental health, (4) detecting or predicting COVID-19 cases, (5) analyzing government responses to the pandemic, and (6) evaluating quality of health information in prevention education videos. The findings also suggested knowledge gaps in real-time COVID-19 surveillance using social media data and limited machine learning or artificial intelligence techniques used in overall COVID-19 research using social media data except the first theme. In the conceptual paper (Study II), a new conceptual framework—social media infodemic listening for public health behaviors (SoMeIL) —was proposed to address limitations in existing theories given lacking systematic and theoretical foundation for such research. After the SoMeIL was proposed, validations were needed. A preliminary qualitative validation and demonstration using Twitter data about the Canadian Freedom Convoy were conductedto partially validate and illustrate how the SoMeIL conceptual framework could be applied (Study III). Finally, the findings from SEM in the last study (Study IV) showed statistically significant associations between the latent variable and the observed variables derived from Twitter. This study provided preliminary evidence to validate partial components in the proposed SoMeIL conceptual framework that could be used as a proxy to infer people’s vaccination intentions in real life. It also demonstrated the feasibility of using Twitter data in SEM research besides typical surveys. Conclusion The scoping review (Study I) was important since it identified various roles that social media data have played in research related to the COVID-19 pandemic. It also informed us of knowledge gaps to be bridged. This led us to the conceptual paper (Study II) since we identified limitations in existing theories when the current theories or theoretical constructs were applied in health research that analyzed social media data. A new conceptual framework—SoMeIL—was proposed accordingly. A preliminary qualitative study was followed to validate and demonstrate partial components of the SoMeIL conceptual framework. The last study (Study IV) showed preliminary evidence to show that parts of the SoMeIL conceptual framework was workable given statistically significant relationships found among certain constructs. As a result, Twitter data in this dissertation could be used as a proxy to infer people’s vaccination behavior in real life as suggested by the proposed conceptual framework. Yet more research is needed to further validate and improve the proposed SoMeIL conceptual framework. If social media listening can be integrated into future pandemic preparedness as the proposed conceptual framework suggests, it can help health authorities and governmental agencies promptly shape public perception, disseminate more scientific information, and influence behaviors during a health crisis in a timely fashion

Image Understanding by Socializing the Semantic Gap

Several technological developments like the Internet, mobile devices and Social Networks have spurred the sharing of images in unprecedented volumes, making tagging and commenting a common habit. Despite the recent progress in image analysis, the problem of Semantic Gap still hinders machines in fully understand the rich semantic of a shared photo. In this book, we tackle this problem by exploiting social network contributions. A comprehensive treatise of three linked problems on image annotation is presented, with a novel experimental protocol used to test eleven state-of-the-art methods. Three novel approaches to annotate, under stand the sentiment and predict the popularity of an image are presented. We conclude with the many challenges and opportunities ahead for the multimedia community