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COVID-19-FAKES: A Twitter (Arabic/English) Dataset for Detecting Misleading Information on COVID-19

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Advances in Intelligent Networking and Collaborative Systems (INCoS 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1263))

Abstract

This paper aims to aid the ongoing research efforts for combating the Infodemic related to COVID-19. We provide an automatically annotated, bilingual (Arabic/English) COVID-19 Twitter dataset (COVID-19-FAKES). This dataset has been continuously collected from February 04, 2020, to March 10, 2020. For annotating the collected dataset, we utilized the shared information on the official websites and the official Twitter accounts of the WHO, UNICEF, and UN as a source of reliable information, and the collected COVID-19 pre-checked facts from different fact-checking websites to build a ground-truth database. Then, the Tweets in the COVID-19-FAKES dataset are annotated using 13 different machine learning algorithms and employing 7 different feature extraction techniques. We are making our dataset publicly available to the research community (https://github.com/mohaddad/COVID-FAKES). This work will help researchers in understanding the dynamics behind the COVID-19 outbreak on Twitter. Furthermore, it could help in studies related to sentiment analysis, the analysis of the propagation of misleading information related to this outbreak, the analysis of users’ behavior during the crisis, the detection of botnets, the analysis of the performance of different classification algorithms with various feature extraction techniques that are used in text mining. It is worth noting that, in this paper, we use the terms of misleading information, misinformation, and fake news interchangeably.

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Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Victoria, Victoria, BC, Canada

    Mohamed K. Elhadad, Kin Fun Li & Fayez Gebali

Authors
  1. Mohamed K. Elhadad
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  2. Kin Fun Li
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  3. Fayez Gebali
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Corresponding author

Correspondence to Kin Fun Li .

Editor information

Editors and Affiliations

  1. Department of Information and Communication Engineering Faculty of Information Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

  2. Department of Electrical and Computer Engineering, University of Victoria, Victoria, BC, Canada

    Kin Fun Li

  3. School of Science and Technology, Kwansei Gakuin University, Sanda, Japan

    Hiroyoshi Miwa

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Elhadad, M.K., Li, K.F., Gebali, F. (2021). COVID-19-FAKES: A Twitter (Arabic/English) Dataset for Detecting Misleading Information on COVID-19. In: Barolli, L., Li, K., Miwa, H. (eds) Advances in Intelligent Networking and Collaborative Systems. INCoS 2020. Advances in Intelligent Systems and Computing, vol 1263. Springer, Cham. https://doi.org/10.1007/978-3-030-57796-4_25

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