@inproceedings{milner-etal-2026-browser,
    title = "A Browser-based Open Source Assistant for Multimodal Content Verification",
    author = "Milner, Rosanna  and
      Foster, Michael  and
      Razuvayevskaya, Olesya  and
      Porcellini, Valentin  and
      Teyssou, Denis  and
      Roberts, Ian  and
      Bontcheva, Kalina",
    editor = "Croce, Danilo  and
      Leidner, Jochen  and
      Moosavi, Nafise Sadat",
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 3: System Demonstrations)",
    month = mar,
    year = "2026",
    address = "Rabat, Marocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.eacl-demo.12/",
    pages = "154--162",
    ISBN = "979-8-89176-382-1",
    abstract = "Disinformation and advanced generative AI content pose a significant challenge for journalists and fact-checkers who must rapidly verify digital media. While many NLP models exist for detecting signals like persuasion techniques, subjectivity, and AI-generated text, they 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, presenting actionable credibility signals, AI-generation likelihood, and other verification advice in an easy-to-digest format. This paper will showcase the tool{'}s architecture, its integration of multiple NLP services, and its real-world application for detecting disinformation."
}