08/12/2020

A Risk Communication Event Detection Model via Contrastive Learning

Mingi Shin, Sungwon Han, Sungkyu Park, Meeyoung Cha

Keywords:

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.

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