A Risk Communication Event Detection Model via Contrastive Learning
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.
PDF Abstract
Datasets
Add Datasets
introduced or used in this paper
Results from the Paper
Submit
results from this paper
to get state-of-the-art GitHub badges and help the
community compare results to other papers.
Methods
No methods listed for this paper. Add
relevant methods here
