MMCoVaR: Multimodal COVID-19 vaccine focused data repository for fake news detection and a baseline architecture for classification

Mingxuan Chen, Xinqiao Chu, K. P. Subbalakshmi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

19 Scopus citations

Abstract

The outbreak of COVID-19 has resulted in an "infodemic"that has encouraged the propagation of misinformation about COVID-19 and cure methods which, in turn, could negatively affect the adoption of recommended public health measures in the larger population. In this paper, we provide a new multimodal (consisting of images, text and temporal information) labeled dataset containing news articles and tweets on the COVID-19 vaccine. We collected 2,593 news articles from 80 publishers for one year between Feb 16th 2020 to May 8th 2021 and 24184 Twitter posts (collected between April 17th 2021 to May 8th 2021). We combine ratings from two news media ranking sites: Medias Bias Chart and Media Bias/Fact Check (MBFC) to classify the news dataset into two levels of credibility: reliable and unreliable. The combination of two filters allows for higher precision of labeling. We also propose a stance detection mechanism to annotate tweets into three levels of credibility: reliable, unreliable and inconclusive. We provide several statistics as well as other analytics like, publisher distribution, publication date distribution, topic analysis, etc. We also provide a novel architecture that classifies the news data into misinformation or truth to provide a baseline performance for this dataset. We find that the proposed architecture has an F-Score of 0.919 and accuracy of 0.882 for fake news detection. Furthermore, we provide benchmark performance for misinformation detection on tweet dataset. This new multimodal dataset can be used in research on COVID-19 vaccine, including misinformation detection, influence of fake COVID-19 vaccine information, etc.

Original languageEnglish
Title of host publicationProceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2021
EditorsMichele Coscia, Alfredo Cuzzocrea, Kai Shu
Pages31-38
Number of pages8
ISBN (Electronic)9781450391283
DOIs
StatePublished - 8 Nov 2021
Event13th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2021 - Virtual, Online, Netherlands
Duration: 8 Nov 2021 → …

Publication series

NameProceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2021

Conference

Conference13th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2021
Country/TerritoryNetherlands
CityVirtual, Online
Period8/11/21 → …

Keywords

  • COVID-19 vaccine
  • explainable model
  • fake news detection
  • misinformation
  • modular architecture
  • multimodal data repository

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