ExFake：基于内容与社交上下文信息的可解释假新闻检测
ExFake: Towards an Explainable Fake News Detection Based on Content and Social Context Information
AI总结：
ExFake结合内容、社交上下文及可信实体数据，并引入可解释AI助手与用户可信度评分机制，在真实数据集上显著优于基线方法，实现可解释的假新闻检测。
摘要：
ExFake是一个基于内容和上下文信息的可解释假新闻检测系统。它关注基于内容、社交上下文（即在线用户的可信度和历史行为）以及来自可信实体（如事实核查网站和命名实体）的数据对在线帖子进行真实性分析。与最先进的系统不同，它还采用了一个可解释AI（XAI）助手，帮助在线社交网络（OSN）用户在面对社交网络上传播的任何可疑信息时培养良好的反应能力。通过为OSN用户分配可信度评分来解决其可信度问题，因为OSN用户是传播假新闻的主要责任人之一。在真实数据集上的实验分析表明，ExFake在假新闻检测方面显著优于其他基线方法。
英文摘要：
ExFake is an explainable fake news detection system based on content and context-level information. It is concerned with the veracity analysis of online posts based on their content, social context (i.e., online users' credibility and historical behaviour), and data coming from trusted entities such as fact-checking websites and named entities. Unlike state-of-the-art systems, an Explainable AI (XAI) assistant is also adopted to help online social networks (OSN) users develop good reflexes when faced with any doubted information that spreads on social networks. The trustworthiness of OSN users is also addressed by assigning a credibility score to OSN users, as OSN users are one of the main culprits for spreading fake news. Experimental analysis on a real-world dataset demonstrates that ExFake significantly outperforms other baseline methods for fake news detection.
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。
