用网页抓取+自然语言推理验证新闻真伪，准确率超传统方法。
VERITAS-NLI : Validation and Extraction of Reliable Information Through Automated Scraping and Natural Language Inference
- 通过抓取外部网页获取信息，用NLI模型判断标题是否属实。
- 在多源新闻数据集上达到84.3%准确率，比BERT高31%。
- 适合需要动态验证信息真实性的新闻审核与舆情监控场景。
当前信息通过网络平台快速传播，假新闻对公共讨论、社会信任和可信新闻来源构成严重威胁。传统机器学习与基于Transformer的模型虽被广泛研究用于假新闻检测，但依赖训练数据且难以泛化到未见标题。为此，我们提出新方案：结合网页抓取技术与自然语言推理（NLI）模型，自动检索验证标题所需外部知识。系统在涵盖多个新闻渠道与广泛领域的自建评估数据集上进行测试。最佳流水线实现84.3%准确率，较最优经典机器学习模型提升33.3%，较BERT提升31.0%。结果表明，动态抓取与NLI结合能有效在外部知识中寻找标题支持，显著提升假新闻检测性能。
原文摘要 · Abstract (English)
In today's day and age where information is rapidly spread through online platforms, the rise of fake news poses an alarming threat to the integrity of public discourse, societal trust, and reputed news sources. Classical machine learning and Transformer-based models have been extensively studied for the task of fake news detection, however they are hampered by their reliance on training data and are unable to generalize on unseen headlines. To address these challenges, we propose our novel solution, leveraging web-scraping techniques and Natural Language Inference (NLI) models to retrieve external knowledge necessary for verifying the accuracy of a headline. Our system is evaluated on a diverse self-curated evaluation dataset spanning over multiple news channels and broad domains. Our best performing pipeline achieves an accuracy of 84.3% surpassing the best classical Machine Learning model by 33.3% and Bidirectional Encoder Representations from Transformers (BERT) by 31.0% . This highlights the efficacy of combining dynamic web-scraping with Natural Language Inference to find support for a claimed headline in the corresponding externally retrieved knowledge for the task of fake news detection.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。