@inproceedings{kumar-etal-2021-narnia,
title = "{NARNIA} at {NLP}4{IF}-2021: Identification of Misinformation in {COVID}-19 Tweets Using {BERT}weet",
author = "Kumar, Ankit and
Jhunjhunwala, Naman and
Agarwal, Raksha and
Chatterjee, Niladri",
editor = "Feldman, Anna and
Da San Martino, Giovanni and
Leberknight, Chris and
Nakov, Preslav",
booktitle = "Proceedings of the Fourth Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.nlp4if-1.14",
doi = "10.18653/v1/2021.nlp4if-1.14",
pages = "99--103",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T NARNIA at NLP4IF-2021: Identification of Misinformation in COVID-19 Tweets Using BERTweet
%A Kumar, Ankit
%A Jhunjhunwala, Naman
%A Agarwal, Raksha
%A Chatterjee, Niladri
%Y Feldman, Anna
%Y Da San Martino, Giovanni
%Y Leberknight, Chris
%Y Nakov, Preslav
%S Proceedings of the Fourth Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F kumar-etal-2021-narnia
%X 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.
%R 10.18653/v1/2021.nlp4if-1.14
%U https://aclanthology.org/2021.nlp4if-1.14
%U https://doi.org/10.18653/v1/2021.nlp4if-1.14
%P 99-103
Markdown (Informal)
[NARNIA at NLP4IF-2021: Identification of Misinformation in COVID-19 Tweets Using BERTweet](https://aclanthology.org/2021.nlp4if-1.14) (Kumar et al., NLP4IF 2021)
ACL