Skeptik: A Hybrid Framework for Combating Potential Misinformation in Journalism
Arlen Fan, Fan Lei, Steven R. Corman, and Ross Maciejewski

TL;DR
Skeptik is a hybrid framework that uses Large Language Models and heuristic methods to detect, annotate, and explain logical fallacies in news articles, aiming to improve media literacy and combat misinformation.
Contribution
It introduces an extensible classification system for logical fallacies, integrating LLMs for real-time analysis within a user-friendly browser extension.
Findings
Effective detection of logical fallacies in news articles
Enhanced user engagement and critical reading skills
Improved media literacy through interactive explanations
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…
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