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A Risk Communication Event Detection Model via Contrastive Learning

Shin, Mingi / Han, Sungwon / Park, Sungkyu / Cha, Meeyoung

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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.

Issue Date
2020-12-12
Publisher
International Committee on Computational Linguistics (ICCL)
URI
https://archives.kdischool.ac.kr/handle/11125/55016
URL
https://aclanthology.org/2020.nlp4if-1.5/
Conf. Name
COLING 2020 Third Workshop on NLP for Internet Freedom (NLP4IF): Censorship, Disinformation, and Propaganda
Place
SP
Conference Date
2020-12-12
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