@inproceedings{biamby-etal-2022-twitter,
    title = "{T}witter-{COMM}s: Detecting Climate, {COVID}, and Military Multimodal Misinformation",
    author = "Biamby, Giscard  and
      Luo, Grace  and
      Darrell, Trevor  and
      Rohrbach, Anna",
    editor = "Carpuat, Marine  and
      de Marneffe, Marie-Catherine  and
      Meza Ruiz, Ivan Vladimir",
    booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jul,
    year = "2022",
    address = "Seattle, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2022.naacl-main.110/",
    doi = "10.18653/v1/2022.naacl-main.110",
    pages = "1530--1549",
    abstract = "Detecting out-of-context media, such as {\textquotedblleft}miscaptioned{\textquotedblright} images on Twitter, is a relevant problem, especially in domains of high public significance. In this work we aim to develop defenses against such misinformation for the topics of Climate Change, COVID-19, and Military Vehicles. We first present a large-scale multimodal dataset with over 884k tweets relevant to these topics. Next, we propose a detection method, based on the state-of-the-art CLIP model, that leverages automatically generated hard image-text mismatches. While this approach works well on our automatically constructed out-of-context tweets, we aim to validate its usefulness on data representative of the real world. Thus, we test it on a set of human-generated fakes, created by mimicking in-the-wild misinformation. We achieve an 11{\%} detection improvement in a high precision regime over a strong baseline. Finally, we share insights about our best model design and analyze the challenges of this emerging threat."
}