This paper is in the following e-collection/theme issue:

Misinformation and Disinformation Outbreaks and Information Prevalence Studies (47) JMIR Theme Issue: COVID-19 Special Issue (2461) Infodemic Management (49) Infodemiology and Infoveillance (1369) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1050) Natural Language Processing (706)

Published on in Vol 3 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/43694, first published .
The Early Detection of Fraudulent COVID-19 Products From Twitter Chatter: Data Set and Baseline Approach Using Anomaly Detection

The Early Detection of Fraudulent COVID-19 Products From Twitter Chatter: Data Set and Baseline Approach Using Anomaly Detection

The Early Detection of Fraudulent COVID-19 Products From Twitter Chatter: Data Set and Baseline Approach Using Anomaly Detection

Authors of this article:

Abeed Sarker1 Author Orcid Image ;   Sahithi Lakamana1 Author Orcid Image ;   Ruqi Liao2 Author Orcid Image ;   Aamir Abbas3 Author Orcid Image ;   Yuan-Chi Yang1 Author Orcid Image ;   Mohammed Al-Garadi1 Author Orcid Image
Article Authors Cited by Tweetations (2) Metrics

Abeed Sarker   1 , PhD ;   Sahithi Lakamana   1 , MSc ;   Ruqi Liao   2 , BS ;   Aamir Abbas   3 , MD, MS ;   Yuan-Chi Yang   1 , PhD ;   Mohammed Al-Garadi   1 , PhD

1 Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA, United States

2 H Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, United States

3 Heinz College of Information Systems and Public Policy, Carnegie Mellon University, Pittsburgh, PA, United States

Corresponding Author:

  • Abeed Sarker, PhD
  • Department of Biomedical Informatics
  • School of Medicine
  • Emory University
  • 101 Woodruff Circle
  • Suite 4101
  • Atlanta, GA, 30030
  • United States
  • Phone: 1 6024746203
  • Email: abeed@dbmi.emory.edu