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Social Network Analysis and Mining

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01-12-2021 | Original Article

CHECKED: Chinese COVID-19 fake news dataset

Authors: Chen Yang, Xinyi Zhou, Reza Zafarani

Published in: Social Network Analysis and Mining | Issue 1/2021

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Abstract

COVID-19 has impacted all lives. To maintain social distancing and avoiding exposure, works and lives have gradually moved online. Under this trend, social media usage to obtain COVID-19 news has increased. Also, misinformation on COVID-19 is frequently spread on social media. In this work, we develop CHECKED, the first Chinese dataset on COVID-19 misinformation. CHECKED provides a total 2,104 verified microblogs related to COVID-19 from December 2019 to August 2020, identified by using a specific list of keywords. Correspondingly, CHECKED includes 1,868,175 reposts, 1,185,702 comments, and 56,852,736 likes that reveal how these verified microblogs are spread and reacted on Weibo. The dataset contains a rich set of multimedia information for each microblog including ground-truth label, textual, visual, temporal, and network information. Extensive experiments have been conducted to analyze CHECKED data and to provide benchmark results for well-established methods when predicting fake news using CHECKED. We hope that CHECKED can facilitate studies that target misinformation on coronavirus. The dataset is available at https://github.com/cyang03/CHECKED.

previous article Identifying Covid-19 misinformation tweets and learning their spatio-temporal topic dynamics using Nonnegative Coupled Matrix Tensor Factorization

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Title: CHECKED: Chinese COVID-19 fake news dataset
Authors: Chen Yang
Xinyi Zhou
Reza Zafarani
Publication date: 01-12-2021
Publisher: Springer Vienna
Published in: Social Network Analysis and Mining / Issue 1/2021
Print ISSN: 1869-5450
Electronic ISSN: 1869-5469
DOI: https://doi.org/10.1007/s13278-021-00766-8

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