@inproceedings{bohacek-2022-misinformation,
    title = "Misinformation Detection in the Wild: News Source Classification as a Proxy for Non-article Texts",
    author = "Bohacek, Matyas",
    editor = "Biester, Laura  and
      Demszky, Dorottya  and
      Jin, Zhijing  and
      Sachan, Mrinmaya  and
      Tetreault, Joel  and
      Wilson, Steven  and
      Xiao, Lu  and
      Zhao, Jieyu",
    booktitle = "Proceedings of the Second Workshop on NLP for Positive Impact (NLP4PI)",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, United Arab Emirates (Hybrid)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2022.nlp4pi-1.10/",
    doi = "10.18653/v1/2022.nlp4pi-1.10",
    pages = "79--88",
    abstract = "Creating classifiers of disinformation is time-consuming, expensive, and requires vast effort from experts spanning different fields. Even when these efforts succeed, their roll-out to publicly available applications stagnates. While these models struggle to find their consumer-accessible use, disinformation behavior online evolves at a pressing speed. The hoaxes get shared in various abbreviations on social networks, often in user-restricted areas, making external monitoring and intervention virtually impossible. To re-purpose existing NLP methods for the new paradigm of sharing misinformation, we propose leveraging information about given texts' originating news sources to proxy the respective text{'}s trustworthiness. We first present a methodology for determining the sources' overall credibility. We demonstrate our pipeline construction in a specific language and introduce CNSC: a novel dataset for Czech articles' news source and source credibility classification. We constitute initial benchmarks on multiple architectures. Lastly, we create in-the-wild wrapper applications of the trained models: a chatbot, a browser extension, and a standalone web application."
}