Fighting against Fake News on Newly-Emerging Crisis: A Case Study of COVID-19-Reference-Cited by-同舟云学术

Fighting against Fake News on Newly-Emerging Crisis: A Case Study of COVID-19

Published:2024-05-13 Issue: Volume:1 Page:718-721
ISSN:
Container-title:Companion Proceedings of the ACM Web Conference 2024
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Author:

Yang Migyeong¹^ORCID,Park Chaewon¹^ORCID,Kang Jiwon¹^ORCID,Lee Daeun¹^ORCID,Choi Daejin²^ORCID,Han Jinyoung¹^ORCID

Affiliation:

1. Sungkyunkwan University, Seoul, Republic of Korea

2. Incheon National University, Incheon, Republic of Korea

Funder

Institute of Information & communications Technology Planning & Evaluation

Electronics and Telecommunications Research Institute

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3589335.3651506

Reference21 articles.

1. Danroujing Chen, et al. 2023. CNFRD: A Few-Shot Rumor Detection Framework via Capsule Network for COVID-19. International Journal of Intelligent Systems 2023 (2023).

2. Limeng Cui and Dongwon Lee. 2020. Coaid: Covid-19 healthcare misinformation dataset. arXiv preprint arXiv:2006.00885 (2020).

3. Sourya Dipta Das, et al. 2021. A heuristic-driven ensemble framework for COVID- 19 fake news detection. In Combating Online Hostile Posts in Regional Languages during Emergency Situation: First International Workshop, CONSTRAINT 2021, Collocated with AAAI 2021, Virtual Event, February 8, 2021, Revised Selected Papers 1. Springer, 164--176.

4. Arkadipta De and Maunendra Sankar Desarkar. 2022. Multi-Context Based Neural Approach for COVID-19 Fake-News Detection. In Companion Proceedings of the Web Conference 2022. 852--859.

5. Jacob Devlin, et al. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the NACCL: Human Language Technologies, Volume 1. 4171--4186.