Verify as You Go: An LLM-Powered Browser Extension for Fake News Detection

TL;DR

This paper presents Aletheia, a browser extension utilizing LLMs and RAG to detect fake news, provide explanations, and engage users through discussion and fact-checking features, demonstrating improved performance and usability.

Contribution

Introducing Aletheia, a novel LLM-powered browser extension that enhances fake news detection with transparency, user interaction, and evidence-based explanations, outperforming existing tools.

Findings

Aletheia outperforms state-of-the-art fake news detection baselines.

User study with 250 participants confirms high usability and perceived effectiveness.

The system effectively combines detection, explanation, and user engagement features.

Abstract

The rampant spread of fake news in the digital age poses serious risks to public trust and democratic institutions, underscoring the need for effective, transparent, and user-centered detection tools. Existing browser extensions often fall short due to opaque model behavior, limited explanatory support, and a lack of meaningful user engagement. This paper introduces Aletheia, a novel browser extension that leverages Retrieval-Augmented Generation (RAG) and Large Language Models (LLMs) to detect fake news and provide evidence-based explanations. Aletheia further includes two interactive components: a Discussion Hub that enables user dialogue around flagged content and a Stay Informed feature that surfaces recent fact-checks. Through extensive experiments, we show that Aletheia outperforms state-of-the-art baselines in detection performance. Complementing this empirical evaluation, a complementary user study with 250 participants confirms the system's usability and perceived effectiveness, highlighting its potential as a transparent tool for combating online fake news.