Detecting Chinese Fake News on Twitter during the COVID-19 Pandemic

TL;DR

This paper presents a new classifier and datasets for detecting Chinese-language fake news about China on Twitter during COVID-19, aiding research on misinformation dynamics.

Contribution

The paper introduces a novel classifier, a labeled dataset of 18,425 tweets, and a generated dataset to study Chinese fake news during the pandemic.

Findings

Classifier achieves 0.64 F1 score and 93% accuracy.

Provides a new dataset for Chinese fake news research.

Tracks fake news dynamics during early COVID-19 pandemic.

Abstract

The outbreak of COVID-19 has led to a global surge of Sinophobia partly because of the spread of misinformation, disinformation, and fake news on China. In this paper, we report on the creation of a novel classifier that detects whether Chinese-language social media posts from Twitter are related to fake news about China. The classifier achieves an F1 score of 0.64 and an accuracy rate of 93%. We provide the final model and a new training dataset with 18,425 tweets for researchers to study fake news in the Chinese language during the COVID-19 pandemic. We also introduce a new dataset generated by our classifier that tracks the dynamics of fake news in the Chinese language during the early pandemic.