@inproceedings{shin-etal-2020-risk,
    title = "A Risk Communication Event Detection Model via Contrastive Learning",
    author = "Shin, Mingi  and
      Han, Sungwon  and
      Park, Sungkyu  and
      Cha, Meeyoung",
    editor = "Da San Martino, Giovanni  and
      Brew, Chris  and
      Ciampaglia, Giovanni Luca  and
      Feldman, Anna  and
      Leberknight, Chris  and
      Nakov, Preslav",
    booktitle = "Proceedings of the 3rd NLP4IF Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics (ICCL)",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2020.nlp4if-1.5/",
    pages = "39--43",
    abstract = "This paper presents a time-topic cohesive model describing the communication patterns on the coronavirus pandemic from three Asian countries. The strength of our model is two-fold. First, it detects contextualized events based on topical and temporal information via contrastive learning. Second, it can be applied to multiple languages, enabling a comparison of risk communication across cultures. We present a case study and discuss future implications of the proposed model."
}