通过实体关联分析提升新闻可信度判断准确率
SemCAFE: When Named Entities make the Difference Assessing Web Source Reliability through Entity-level Analytics
- 基于实体语义关系构建新闻文章的语义指纹
- 在俄乌战争相关文章上实现12%的宏观F1提升
- 适合关注虚假信息检测与数字媒体可信度评估者
随着传统媒体向数字媒体转变，网络环境不仅包含可靠新闻，还充斥大量不可靠内容。数字媒体传播迅速，显著影响公众舆论并推动政治议程。尽管读者可能熟悉其偏好的媒体立场或可信度，但识别不可靠新闻仍具挑战性。许多在线来源的可信度不透明，且人工智能生成内容可低成本传播。2022年俄乌战争期间的不可靠新闻与可信来源在主题和写作风格上高度相似，难以区分。为此，我们提出SemCAFE系统，通过引入实体相关性来评估新闻可靠性。该系统采用标准自然语言处理技术（如去噪、分词）及基于YAGO知识库的实体级语义分析，为每篇新闻创建语义指纹。在46,020篇可靠与3,407篇不可靠文章上测试，相较现有最佳方法，宏平均F1分数提升12%。样本数据与代码已公开于GitHub。
原文摘要 · Abstract (English)
With the shift from traditional to digital media, the online landscape now hosts not only reliable news articles but also a significant amount of unreliable content. Digital media has faster reachability by significantly influencing public opinion and advancing political agendas. While newspaper readers may be familiar with their preferred outlets political leanings or credibility, determining unreliable news articles is much more challenging. The credibility of many online sources is often opaque, with AI generated content being easily disseminated at minimal cost. Unreliable news articles, particularly those that followed the Russian invasion of Ukraine in 2022, closely mimic the topics and writing styles of credible sources, making them difficult to distinguish. To address this, we introduce SemCAFE, a system designed to detect news reliability by incorporating entity relatedness into its assessment. SemCAFE employs standard Natural Language Processing techniques, such as boilerplate removal and tokenization, alongside entity level semantic analysis using the YAGO knowledge base. By creating a semantic fingerprint for each news article, SemCAFE could assess the credibility of 46,020 reliable and 3,407 unreliable articles on the 2022 Russian invasion of Ukraine. Our approach improved the macro F1 score by 12% over state of the art methods. The sample data and code are available on GitHub
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