用文章时间序列信号预测新网站可信度，无需先验知识
Can temporal article-level credibility signals improve domain-level credibility prediction?
- 基于专家评分构建时序评估框架，模拟人工核查流程
- 首次验证可通过文章内容动态推断域名整体可信度
- 适合需自动化打假的新闻平台与信息监管系统
网络域名可信度评估对遏制虚假信息至关重要。传统方法依赖域名类型、透明度和声誉，但新兴域名因无历史记录而难以评估。专家通过分析文章内容（如虚假信息、偏见或宣传）判断可信度。然而大模型催生大规模内容生成，使人工评估力不从心，亟需自动化方案。本文提出领域可信度评估框架（DCEF），基于专家评分建立时序框架，探索能否仅通过已发布文章内容，在无任何源域名先验信息的情况下，模仿专家流程评估域名整体可信度。
原文摘要 · Abstract (English)
Web domain credibility evaluation is vital for combating misinformation. It is conducted by examining factors such as domain type, transparency, and overall reputation. However, assessing the credibility of newly emerging web domains remains challenging since they have no reputation yet. Expert fact-checkers evaluate the credibility of domains by analyzing the content of their articles, including the presence of misinformation, bias, or propaganda. Yet, the ease of large-scale content generation enabled by LLMs has accelerated the creation of new content, rendering manual assessment insufficient and underscoring the need for automated approaches to domain credibility evaluation. In this paper, we introduce our Domain Credibility Evaluation Framework (DCEF), a temporal framework for domain credibility evaluation grounded in expert ratings. DCEF enables us to investigate whether the credibility of web domains can be assessed from their published articles following the workflow of expert fact-checkers, without any prior knowledge of the source domains themselves.
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