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The outbreak of COVID-19 has resulted in an "infodemic" that has encouragedthe propagation of misinformation about COVID-19 and cure methods which, inturn, could negatively affect the adoption of recommended public healthmeasures in the larger population. In this paper, we provide a new multimodal(consisting of images, text and temporal information) labeled datasetcontaining news articles and tweets on the COVID-19 vaccine. We collected 2,593news articles from 80 publishers for one year between Feb 16th 2020 to May 8th2021 and 24184 Twitter posts (collected between April 17th 2021 to May 8th2021). We combine ratings from two news media ranking sites: Medias Bias Chartand Media Bias/Fact Check (MBFC) to classify the news dataset into two levelsof credibility: reliable and unreliable. The combination of two filters allowsfor higher precision of labeling. We also propose a stance detection mechanismto annotate tweets into three levels of credibility: reliable, unreliable andinconclusive. We provide several statistics as well as other analytics like,publisher distribution, publication date distribution, topic analysis, etc. Wealso provide a novel architecture that classifies the news data intomisinformation or truth to provide a baseline performance for this dataset. Wefind that the proposed architecture has an F-Score of 0.919 and accuracy of0.882 for fake news detection. Furthermore, we provide benchmark performancefor misinformation detection on tweet dataset. This new multimodal dataset canbe used in research on COVID-19 vaccine, including misinformation detection,influence of fake COVID-19 vaccine information, etc.

MMCoVaR: Multimodal COVID-19 Vaccine Focused Data Repository for Fake News Detection and a Baseline Architecture for Classification