融合内容与社交关系的可解释假新闻检测框架
Exploring Content and Social Connections of Fake News with Explainable Text and Graph Learning
- 结合文本、社交互动与图结构特征进行假新闻识别
- 多语言数据集上准确率优于单一模态方法
- 生成人类可理解的判断理由，适合可信度评估场景
虚假信息的全球传播及内容可信度问题推动了自动化事实核查系统的发展。由于虚假信息常利用社交媒体中的点赞和用户网络等机制扩大影响，有效解决方案必须超越内容分析，纳入社交因素。单纯标记内容为虚假可能无效甚至强化自动化偏见与确认偏误。本文提出一种可解释框架，融合内容、社交媒体与图结构特征以提升事实核查能力。该框架将虚假信息分类器与可解释技术结合，提供完整且可理解的决策依据。实验表明，多模态信息相比单模态显著提升性能，在英文、西班牙语和葡萄牙语数据集上均验证有效。此外，通过新设计的评估协议，对框架生成的解释进行了可读性、可信度和鲁棒性测试，结果表明其能有效生成人类可理解的预测理由。
原文摘要 · Abstract (English)
The global spread of misinformation and concerns about content trustworthiness have driven the development of automated fact-checking systems. Since false information often exploits social media dynamics such as "likes" and user networks to amplify its reach, effective solutions must go beyond content analysis to incorporate these factors. Moreover, simply labelling content as false can be ineffective or even reinforce biases such as automation and confirmation bias. This paper proposes an explainable framework that combines content, social media, and graph-based features to enhance fact-checking. It integrates a misinformation classifier with explainability techniques to deliver complete and interpretable insights supporting classification decisions. Experiments demonstrate that multimodal information improves performance over single modalities, with evaluations conducted on datasets in English, Spanish, and Portuguese. Additionally, the framework's explanations were assessed for interpretability, trustworthiness, and robustness with a novel protocol, showing that it effectively generates human-understandable justifications for its predictions.
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