MCFEND: A Multi-source Benchmark Dataset for Chinese Fake News Detection
This addresses the problem of improving fake news detection for Chinese online content by providing a more realistic dataset, though it is incremental as it builds on existing detection methods.
The paper tackles the problem of Chinese fake news detection by constructing MCFEND, the first multi-source benchmark dataset, because existing datasets are limited to Weibo and methods trained on them perform poorly on multi-source data, with F1 scores dropping from 0.943 to 0.470. The result is a dataset collected from diverse sources and fact-checked by 14 agencies, enabling evaluation of detection methods in real-world scenarios.
The prevalence of fake news across various online sources has had a significant influence on the public. Existing Chinese fake news detection datasets are limited to news sourced solely from Weibo. However, fake news originating from multiple sources exhibits diversity in various aspects, including its content and social context. Methods trained on purely one single news source can hardly be applicable to real-world scenarios. Our pilot experiment demonstrates that the F1 score of the state-of-the-art method that learns from a large Chinese fake news detection dataset, Weibo-21, drops significantly from 0.943 to 0.470 when the test data is changed to multi-source news data, failing to identify more than one-third of the multi-source fake news. To address this limitation, we constructed the first multi-source benchmark dataset for Chinese fake news detection, termed MCFEND, which is composed of news we collected from diverse sources such as social platforms, messaging apps, and traditional online news outlets. Notably, such news has been fact-checked by 14 authoritative fact-checking agencies worldwide. In addition, various existing Chinese fake news detection methods are thoroughly evaluated on our proposed dataset in cross-source, multi-source, and unseen source ways. MCFEND, as a benchmark dataset, aims to advance Chinese fake news detection approaches in real-world scenarios.