Abstract
With the prevalence of social media, fake news has become one of the greatest challenges in journalism, which has weakened public trust in news outlets and authorities. During the COVID-19 epidemic, the widely circulated pandemic-related fake news on social media misleads or threatens the public. Recent works have investigated fake news detection on social platforms in English and Mandarin, though Cantonese fake news has been understudied. To pave the way for Cantonese COVID-19 fake news detection, we first presented an annotated COVID-19 related Cantonese fake news dataset collected from a popular local discussion forum in Hong Kong. Then, we explored the dataset by applying topic modeling to identify the topics that contain the most significant amount of fake news. Moreover, we evaluated both traditional machine learning algorithms and deep learning algorithms for Cantonese fake news detection. Our empirical results show that deep learning based methods perform slightly better than traditional machine learning methods on TF-IDF features.
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