A Browser-based Open Source Assistant for Multimodal Content Verification
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| author | Milner, Rosanna Foster, Michael Karmakharm, Twin Razuvayevskaya, Olesya Roberts, Ian Porcellini, Valentin Teyssou, Denis Bontcheva, Kalina |
| author_facet | Milner, Rosanna Foster, Michael Karmakharm, Twin Razuvayevskaya, Olesya Roberts, Ian Porcellini, Valentin Teyssou, Denis Bontcheva, Kalina |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_02842 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Browser-based Open Source Assistant for Multimodal Content Verification Milner, Rosanna Foster, Michael Karmakharm, Twin Razuvayevskaya, Olesya Roberts, Ian Porcellini, Valentin Teyssou, Denis Bontcheva, Kalina Computation and Language 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. |
| title | A Browser-based Open Source Assistant for Multimodal Content Verification |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2603.02842 |