@inproceedings{requena-etal-2026-multimodal,
    title = "A Multimodal Framework for Financial Fake News Detection for {B}razilian {P}ortuguese",
    author = "Requena, Jos{\'e} Vitor Souza Cardoso  and
      Ribeiro, Jo{\~a}o Victor Assaoka  and
      Berton, Lilian",
    editor = "Souza, Marlo  and
      de-Dios-Flores, Iria  and
      Santos, Diana  and
      Freitas, Larissa  and
      Souza, Jackson Wilke da Cruz  and
      Ribeiro, Eug{\'e}nio",
    booktitle = "Proceedings of the 17th International Conference on Computational Processing of {P}ortuguese ({PROPOR} 2026) - Vol. 1",
    month = apr,
    year = "2026",
    address = "Salvador, Brazil",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-dnd/2026.propor-1.23/",
    pages = "234--239",
    ISBN = "979-8-89176-387-6",
    abstract = "The rapid dissemination of digital information has exposed financial markets to the risks of disinformation. Although numerous methods exist to detect fake news, they predominantly focus on textual features, often neglecting the significant role of image-based content. This paper introduces a novel framework for detecting financial fake news in Brazilian Portuguese by bridging this gap. The proposed system integrates Natural Language Processing (NLP) with an image-to-text classification strategy: using a Tesseract-based OCR, the system extracts text from images and processes it using the unified pipeline used for text classification. Experiments on Fake.BR, FakeRecogna corpus and BBC News Brasil show that our approach achieves 98{\%} accuracy using BERTimbau Fine Tuned on financial news. These findings underscore the critical importance of analyzing visual text and demonstrate the multimodal strategy is effective for disinformation detection."
}