@inproceedings{lwowski-najafirad-2020-covid,
    title = "{COVID}-19 Surveillance through {T}witter using Self-Supervised and Few Shot Learning",
    author = "Lwowski, Brandon  and
      Najafirad, Peyman",
    editor = "Verspoor, Karin  and
      Cohen, Kevin Bretonnel  and
      Conway, Michael  and
      de Bruijn, Berry  and
      Dredze, Mark  and
      Mihalcea, Rada  and
      Wallace, Byron",
    booktitle = "Proceedings of the 1st Workshop on {NLP} for {COVID}-19 (Part 2) at {EMNLP} 2020",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2020.nlpcovid19-2.9/",
    doi = "10.18653/v1/2020.nlpcovid19-2.9",
    abstract = "Public health surveillance and tracking virus via social media can be a useful digital tool for contact tracing and preventing the spread of the virus. Nowadays, large volumes of COVID-19 tweets can quickly be processed in real-time to offer information to researchers. Nonetheless, due to the absence of labeled data for COVID-19, the preliminary supervised classifier or semi-supervised self-labeled methods will not handle non-spherical data with adequate accuracy. With the seasonal influenza and novel Coronavirus having many similar symptoms, we propose using few shot learning to fine-tune a semi-supervised model built on unlabeled COVID-19 and previously labeled influenza dataset that can provide in- sights into COVID-19 that have not been investigated. The experimental results show the efficacy of the proposed model with an accuracy of 86{\%}, identification of Covid-19 related discussion using recently collected tweets."
}