Title: Skeptik: A Hybrid Framework for Combating Potential Misinformation in Journalism Show Chinese title

Title: 怀疑论者：一种用于应对新闻中潜在虚假信息的混合框架

Abstract: The proliferation of misinformation in journalism, often stemming from flawed reasoning and logical fallacies, poses significant challenges to public understanding and trust in news media. Traditional fact-checking methods, while valuable, are insufficient for detecting the subtle logical inconsistencies that can mislead readers within seemingly factual content. To address this gap, we introduce Skeptik, a hybrid framework that integrates Large Language Models (LLMs) with heuristic approaches to analyze and annotate potential logical fallacies and reasoning errors in online news articles. Operating as a web browser extension, Skeptik automatically highlights sentences that may contain logical fallacies, provides detailed explanations, and offers multi-layered interventions to help readers critically assess the information presented. The system is designed to be extensible, accommodating a wide range of fallacy types and adapting to evolving misinformation tactics. Through comprehensive case studies, quantitative analyses, usability experiments, and expert evaluations, we demonstrate the effectiveness of Skeptik in enhancing readers' critical examination of news content and promoting media literacy. Our contributions include the development of an expandable classification system for logical fallacies, the innovative integration of LLMs for real-time analysis and annotation, and the creation of an interactive user interface that fosters user engagement and close reading. By emphasizing the logical integrity of textual content rather than relying solely on factual accuracy, Skeptik offers a comprehensive solution to combat potential misinformation in journalism. Ultimately, our framework aims to improve critical reading and protect the public from deceptive information online and enhance the overall credibility of news media. Show Chinese abstract

Abstract: 新闻报道中虚假信息的泛滥，往往源于错误的推理和逻辑谬误，这对公众对新闻媒体的理解和信任构成了重大挑战。传统的事实核查方法虽然有价值，但不足以检测出可能在看似事实性的内容中误导读者的细微逻辑不一致之处。为弥补这一差距，我们引入了Skeptik，这是一个混合框架，将大型语言模型（LLMs）与启发式方法相结合，以分析和标注在线新闻文章中的潜在逻辑谬误和推理错误。作为网络浏览器插件运行，Skeptik会自动突出显示可能包含逻辑谬误的句子，提供详细的解释，并提供多层干预措施，帮助读者批判性地评估所呈现的信息。该系统设计为可扩展，能够容纳各种类型的谬误，并适应不断演变的虚假信息策略。通过全面的案例研究、定量分析、可用性实验和专家评估，我们展示了Skeptik在增强读者对新闻内容的批判性审查和促进媒体素养方面的有效性。我们的贡献包括开发了一个可扩展的逻辑谬误分类系统，创新性地将LLMs用于实时分析和标注，以及创建了一个互动用户界面，以促进用户参与和仔细阅读。通过强调文本内容的逻辑完整性，而不是仅仅依赖事实准确性，Skeptik为应对新闻报道中的潜在虚假信息提供了全面的解决方案。最终，我们的框架旨在提高批判性阅读能力，保护公众免受在线欺骗性信息的影响，并提升新闻媒体的整体可信度。