A Browser-based Open Source Assistant for Multimodal Content Verification

TL;DR

The VERIFICATION ASSISTant, a browser-based tool designed to bridge the gap between NLP models for detecting credibility signals and real-world application to detecting disinformation, is demonstrated.

Abstract

Disinformation and false content produced by generative AI pose a significant challenge for journalists and fact-checkers who must rapidly verify digital media information. While there is an abundance of NLP models for detecting credibility signals such as persuasion techniques, subjectivity, or machine-generated text, such methods often remain inaccessible to non-expert users and are not integrated into their daily workflows as a unified framework. This paper demonstrates the VERIFICATION ASSISTANT, a browser-based tool designed to bridge this gap. The VERIFICATION ASSISTANT, a core component of the widely adopted VERIFICATION PLUGIN (140,000+ users), allows users to submit URLs or media files to a unified interface. It automatically extracts content and routes it to a suite of backend NLP classifiers, delivering actionable credibility signals, estimating AI-generated content, and providing other verification guidance in a clear, easy-to-digest format. This paper showcases the tool architecture, its integration of multiple NLP services, and its real-world application to detecting disinformation.

Figures (9)

• Figure 1: Requests sent by the assistant when checking a typical webpage.
• Figure 2: DBKF text service and Fact Check Semantic Search for simulated data.
• Figure 3: URL domain analysis results.
• Figure 4: Recommended tools for an image.
• Figure 5: Credibility signals on an AI-generated article.