@inproceedings{kumar-etal-2021-narnia,
    title = "{NARNIA} at {NLP}4{IF}-2021: Identification of Misinformation in {COVID}-19 Tweets Using {BERT}weet",
    author = "Kumar, Ankit  and
      Jhunjhunwala, Naman  and
      Agarwal, Raksha  and
      Chatterjee, Niladri",
    editor = "Feldman, Anna  and
      Da San Martino, Giovanni  and
      Leberknight, Chris  and
      Nakov, Preslav",
    booktitle = "Proceedings of the Fourth Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda",
    month = jun,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Author-page-Marten-During-lu/2021.nlp4if-1.14/",
    doi = "10.18653/v1/2021.nlp4if-1.14",
    pages = "99--103",
    abstract = "The spread of COVID-19 has been accompanied with widespread misinformation on social media. In particular, Twitterverse has seen a huge increase in dissemination of distorted facts and figures. The present work aims at identifying tweets regarding COVID-19 which contains harmful and false information. We have experimented with a number of Deep Learning-based models, including different word embeddings, such as Glove, ELMo, among others. BERTweet model achieved the best overall F1-score of 0.881 and secured the third rank on the above task."
}