NARNIA at NLP4IF-2021: Identification of Misinformation in COVID-19 Tweets Using BERTweet

Ankit Kumar, Naman Jhunjhunwala, Raksha Agarwal, Niladri Chatterjee


Abstract
The spread of COVID-19 has been accompanied with widespread misinformation on social media. In particular, Twitterverse has seen a huge increase in dissemination of distorted facts and figures. The present work aims at identifying tweets regarding COVID-19 which contains harmful and false information. We have experimented with a number of Deep Learning-based models, including different word embeddings, such as Glove, ELMo, among others. BERTweet model achieved the best overall F1-score of 0.881 and secured the third rank on the above task.
Anthology ID:
2021.nlp4if-1.14
Volume:
Proceedings of the Fourth Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda
Month:
June
Year:
2021
Address:
Online
Venue:
NLP4IF
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
99–103
Language:
URL:
https://aclanthology.org/2021.nlp4if-1.14
DOI:
10.18653/v1/2021.nlp4if-1.14
Bibkey:
Cite (ACL):
Ankit Kumar, Naman Jhunjhunwala, Raksha Agarwal, and Niladri Chatterjee. 2021. NARNIA at NLP4IF-2021: Identification of Misinformation in COVID-19 Tweets Using BERTweet. In Proceedings of the Fourth Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda, pages 99–103, Online. Association for Computational Linguistics.
Cite (Informal):
NARNIA at NLP4IF-2021: Identification of Misinformation in COVID-19 Tweets Using BERTweet (Kumar et al., NLP4IF 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.nlp4if-1.14.pdf