MCFEND：一个用于中文假新闻检测的多源基准数据集
MCFEND: A Multi-source Benchmark Dataset for Chinese Fake News Detection
AI总结：
针对现有中文假新闻检测数据集仅限单一来源导致跨源性能下降的问题，本文构建了首个多源基准数据集MCFEND，并全面评估了现有方法在跨源、多源和未见源场景下的表现。
摘要：
各种在线来源假新闻的盛行对公众产生了重大影响。现有的中文假新闻检测数据集仅限于源自微博的新闻。然而，来自多个来源的假新闻在内容和社交语境等多个方面表现出多样性。仅在单一新闻来源上训练的方法难以适用于真实场景。我们的初步实验表明，当测试数据更改为多源新闻数据时，从大型中文假新闻检测数据集Weibo-21中学习的最先进方法的F1分数从0.943显著下降至0.470，未能识别超过三分之一的多源假新闻。为了解决这一局限性，我们构建了首个用于中文假新闻检测的多源基准数据集，称为MCFEND，该数据集由我们从社交媒体平台、通讯应用和传统在线新闻媒体等多种来源收集的新闻组成。值得注意的是，此类新闻已由全球14个权威事实核查机构进行了事实核查。此外，我们以跨源、多源和未见源的方式在我们提出的数据集上对现有的各种中文假新闻检测方法进行了全面评估。MCFEND作为基准数据集，旨在推进真实场景中的中文假新闻检测方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
