Skeptik: A Hybrid Framework for Combating Potential Misinformation in Journalism

2025-08-25

AI Summary

Misinformation containing implicit logical fallacies in news articles severely undermines public comprehension and erodes media trust, yet conventional fact-checking methods struggle to detect such covert inferential flaws. To address this, we propose a scalable hybrid framework: (1) a fine-grained taxonomy of logical fallacies, (2) an automated detection and annotation pipeline integrating large language models (LLMs) with heuristic rule-based reasoning, and (3) an interactive browser extension enabling multi-level cognitive interventions to foster deep reading and critical evaluation. Experimental results demonstrate that the system significantly improves readers’ ability to identify logical inconsistencies in news content. Expert evaluations confirm its effectiveness, practicality, and scalability for media literacy education, particularly in supporting pedagogical scaffolding and real-time analytical feedback during news consumption.

Technology Category

• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Application Domains: Misinformation & Fake News
• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Mitigating misinformation and disinformation
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

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.

Problem

• Detects logical fallacies in online news articles
• Combats misinformation by analyzing reasoning errors
• Enhances critical reading through real-time annotations

Innovation

• Hybrid framework combining LLMs with heuristic methods
• Real-time logical fallacy detection via browser extension
• Extensible classification system for evolving misinformation tactics