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.- Anthology ID:
- 2020.nlp4if-1.5
- Volume:
- Proceedings of the 3rd NLP4IF Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda
- Month:
- December
- Year:
- 2020
- Address:
- Barcelona, Spain (Online)
- Editors:
- Giovanni Da San Martino, Chris Brew, Giovanni Luca Ciampaglia, Anna Feldman, Chris Leberknight, Preslav Nakov
- Venue:
- NLP4IF
- SIG:
- Publisher:
- International Committee on Computational Linguistics (ICCL)
- Note:
- Pages:
- 39–43
- Language:
- URL:
- https://aclanthology.org/2020.nlp4if-1.5
- DOI:
- Cite (ACL):
- Mingi Shin, Sungwon Han, Sungkyu Park, and Meeyoung Cha. 2020. A Risk Communication Event Detection Model via Contrastive Learning. In Proceedings of the 3rd NLP4IF Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda, pages 39–43, Barcelona, Spain (Online). International Committee on Computational Linguistics (ICCL).
- Cite (Informal):
- A Risk Communication Event Detection Model via Contrastive Learning (Shin et al., NLP4IF 2020)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2020.nlp4if-1.5.pdf