# MCFEND: A Multi-source Benchmark Dataset for Chinese Fake News Detection

arXiv: https://arxiv.org/abs/2403.09092 · HTML: https://mlsanity.com/paper/2403.09092/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 39 references that are in the map.
- First posted on arXiv 14 Mar 2024.

## Builds on (most cited first, 16 of 16)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- ["Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection](https://mlsanity.com/paper/1705.00648/) (2017)
- [Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks](https://mlsanity.com/paper/1503.00075/) (2015)
- [FakeNewsNet: A Data Repository with News Content, Social Context and Spatialtemporal Information for Studying Fake News on Social Media](https://mlsanity.com/paper/1809.01286/) (2018)
- [Weak Supervision for Fake News Detection via Reinforcement Learning](https://mlsanity.com/paper/1912.12520/) (2019)
- [Ginger Cannot Cure Cancer: Battling Fake Health News with a Comprehensive Data Repository](https://mlsanity.com/paper/2002.00837/) (2020)
- [Integrating Pattern- and Fact-based Fake News Detection via Model Preference Learning](https://mlsanity.com/paper/2109.11333/) (2021)
- [Article Reranking by Memory-Enhanced Key Sentence Matching for Detecting Previously Fact-Checked Claims](https://mlsanity.com/paper/2112.10322/) (2021)
- [Improved Target-specific Stance Detection on Social Media Platforms by Delving into Conversation Threads](https://mlsanity.com/paper/2211.03061/) (2022)
- [CHECKED: Chinese COVID-19 Fake News Dataset](https://mlsanity.com/paper/2010.09029/) (2020)
- [False News On Social Media: A Data-Driven Survey](https://mlsanity.com/paper/1902.07539/) (2019)
- [HFL at SemEval-2022 Task 8: A Linguistics-inspired Regression Model with Data Augmentation for Multilingual News Similarity](https://mlsanity.com/paper/2204.04844/) (2022)

## Built on by (most cited first, 12 of 12)

- [Truth over Tricks: Measuring and Mitigating Shortcut Learning in Misinformation Detection](https://mlsanity.com/paper/2506.02350/) (2025)
- [FineFake: A Knowledge-Enriched Dataset for Fine-Grained Multi-Domain Fake News Detection](https://mlsanity.com/paper/2404.01336/) (2024)
- [Debating Truth: Debate-driven Claim Verification with Multiple Large Language Model Agents](https://mlsanity.com/paper/2507.19090/) (2025)
- [OutSafe-Bench: A Benchmark for Multimodal Offensive Content Detection in Large Language Models](https://mlsanity.com/paper/2511.10287/) (2025)
- [EcoGEO: Trajectory-Aware Evidence Ecosystems for Web-Enabled LLM Search Agents](https://mlsanity.com/paper/2605.12887/) (2026)
- [MPPFND: A Dataset and Analysis of Detecting Fake News with Multi-Platform Propagation](https://mlsanity.com/paper/2505.15834/) (2025)
- [Official-NV: An LLM-Generated News Video Dataset for Multimodal Fake News Detection](https://mlsanity.com/paper/2407.19493/) (2024)
- [SpMis: An Investigation of Synthetic Spoken Misinformation Detection](https://mlsanity.com/paper/2409.11308/) (2024)
- [FMNV: A Dataset of Media-Published News Videos for Fake News Detection](https://mlsanity.com/paper/2504.07687/) (2025)
- [Unveiling Modality Bias: Automated Sample-Specific Analysis for Multimodal Misinformation Benchmarks](https://mlsanity.com/paper/2511.05883/) (2025)
- [Large language models for spreading dynamics in complex systems](https://mlsanity.com/paper/2602.08085/) (2026)
- [OmniVL-Guard Pro: A Tool-Augmented Agent for Omnibus Vision-Language Forensics](https://mlsanity.com/paper/2605.16962/) (2026)

## Abstract (arXiv)

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.