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Enormous hope in the efficacy of vaccines became recently a successful reality in the fight against the COVID-19 pandemic. However, vaccine hesitancy, fueled by exposure to social media misinformation about COVID-19 vaccines became a major hurdle. Therefore, it is essential to automatically detect where misinformation about COVID-19 vaccines on social media is spread and what kind of misinformation is discussed, such that inoculation interventions can be delivered at the right time and in the right place, in addition to interventions designed to address vaccine hesitancy. This paper is addressing the first step in tackling hesitancy against COVID-19 vaccines, namely the automatic detection of known misinformation about the vaccines on Twitter, the social media platform that has the highest volume of conversations about COVID-19 and its vaccines. We present CoVaxLies, a new dataset of tweets judged relevant to several misinformation targets about COVID-19 vaccines on which a novel method of detecting misinformation was developed. Our method organizes CoVaxLies in a Misinformation Knowledge Graph as it casts misinformation detection as a graph link prediction problem. The misinformation detection method detailed in this paper takes advantage of the link scoring functions provided by several knowledge embedding methods. The experimental results demonstrate the superiority of this method when compared with classification-based methods, widely used currently. [ABSTRACT FROM AUTHOR]

Social media vaccine misinformation can negatively influence vaccine attitudes. It is urgent to develop communication approaches to reduce the misinformation's impact. This study aimed to test the effects of fact-checking labels for misinformation on attitudes toward vaccines. An online survey experiment with 1198 participants recruited from a U.S. national sample was conducted in 2018. Participants were randomly assigned to six conditions: misinformation control, or fact-checking label conditions attributed to algorithms, news media, health institutions, research universities, or fact-checking organizations. We analyzed differences in vaccine attitudes between the fact-checking label and control conditions. Further, we compared perceived expertise and trustworthiness of the five categories of fact-checking sources. Fact-checking labels attached to misinformation posts made vaccine attitudes more positive compared to the misinformation control condition (P =.003, Cohen's d= 0.21). Conspiracy ideation moderated the effect of the labels on vaccine attitudes (P =.02). Universities and health institutions were rated significantly higher on source expertise than other sources. Mediation analyses showed labels attributed to universities and health institutions indirectly resulted in more positive attitudes than other sources through perceived expertise. Exposure to fact-checking labels on misinformation can generate more positive attitudes toward vaccines in comparison to exposure to misinformation. Incorporating labels from trusted universities and health institutions on social media platforms is a promising direction for addressing the vaccine misinformation problem. This points to the necessity for closer collaboration between public health and research institutions and social media companies to join efforts in addressing the current misinformation threat. • An online survey experiment to promote vaccine attitude on social media. • The effects of fact-checking label on vaccine misinformation were tested. • The effects of 5 categories of fact-checking label sources were compared. • Fact-checking label on misinformation made vaccine attitude more positive. • Universities and health institutions were rated of higher source expertise. [ABSTRACT FROM AUTHOR]