@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://aclanthology.org/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.",
}
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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.</abstract>
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%0 Conference Proceedings
%T A Risk Communication Event Detection Model via Contrastive Learning
%A Shin, Mingi
%A Han, Sungwon
%A Park, Sungkyu
%A Cha, Meeyoung
%Y Da San Martino, Giovanni
%Y Brew, Chris
%Y Ciampaglia, Giovanni Luca
%Y Feldman, Anna
%Y Leberknight, Chris
%Y Nakov, Preslav
%S Proceedings of the 3rd NLP4IF Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda
%D 2020
%8 December
%I International Committee on Computational Linguistics (ICCL)
%C Barcelona, Spain (Online)
%F shin-etal-2020-risk
%X 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.
%U https://aclanthology.org/2020.nlp4if-1.5
%P 39-43
Markdown (Informal)
[A Risk Communication Event Detection Model via Contrastive Learning](https://aclanthology.org/2020.nlp4if-1.5) (Shin et al., NLP4IF 2020)
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).