@inproceedings{rahman-etal-2024-cuet,
title = "{CUET}{\_}{DUO}@{D}ravidian{L}ang{T}ech {EACL}2024: Fake News Classification Using {M}alayalam-{BERT}",
author = "Rahman, Tanzim and
Raihan, Abu and
Rahman, Md. and
Hossain, Jawad and
Ahsan, Shawly and
Das, Avishek and
Hoque, Mohammed Moshiul",
editor = "Chakravarthi, Bharathi Raja and
Priyadharshini, Ruba and
Madasamy, Anand Kumar and
Thavareesan, Sajeetha and
Sherly, Elizabeth and
Nadarajan, Rajeswari and
Ravikiran, Manikandan",
booktitle = "Proceedings of the Fourth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages",
month = mar,
year = "2024",
address = "St. Julian's, Malta",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.dravidianlangtech-1.37",
pages = "223--228",
abstract = "Identifying between fake and original news in social media demands vigilant procedures. This paper introduces the significant shared task on {`}Fake News Detection in Dravidian Languages - DravidianLangTech@EACL 2024{'}. With a focus on the Malayalam language, this task is crucial in identifying social media posts as either fake or original news. The participating teams contribute immensely to this task through their varied strategies, employing methods ranging from conventional machine-learning techniques to advanced transformer-based models. Notably, the findings of this work highlight the effectiveness of the Malayalam-BERT model, demonstrating an impressive macro F1 score of 0.88 in distinguishing between fake and original news in Malayalam social media content, achieving a commendable rank of 1st among the participants.",
}
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<abstract>Identifying between fake and original news in social media demands vigilant procedures. This paper introduces the significant shared task on ‘Fake News Detection in Dravidian Languages - DravidianLangTech@EACL 2024’. With a focus on the Malayalam language, this task is crucial in identifying social media posts as either fake or original news. The participating teams contribute immensely to this task through their varied strategies, employing methods ranging from conventional machine-learning techniques to advanced transformer-based models. Notably, the findings of this work highlight the effectiveness of the Malayalam-BERT model, demonstrating an impressive macro F1 score of 0.88 in distinguishing between fake and original news in Malayalam social media content, achieving a commendable rank of 1st among the participants.</abstract>
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%0 Conference Proceedings
%T CUET_DUO@DravidianLangTech EACL2024: Fake News Classification Using Malayalam-BERT
%A Rahman, Tanzim
%A Raihan, Abu
%A Rahman, Md.
%A Hossain, Jawad
%A Ahsan, Shawly
%A Das, Avishek
%A Hoque, Mohammed Moshiul
%Y Chakravarthi, Bharathi Raja
%Y Priyadharshini, Ruba
%Y Madasamy, Anand Kumar
%Y Thavareesan, Sajeetha
%Y Sherly, Elizabeth
%Y Nadarajan, Rajeswari
%Y Ravikiran, Manikandan
%S Proceedings of the Fourth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julian’s, Malta
%F rahman-etal-2024-cuet
%X Identifying between fake and original news in social media demands vigilant procedures. This paper introduces the significant shared task on ‘Fake News Detection in Dravidian Languages - DravidianLangTech@EACL 2024’. With a focus on the Malayalam language, this task is crucial in identifying social media posts as either fake or original news. The participating teams contribute immensely to this task through their varied strategies, employing methods ranging from conventional machine-learning techniques to advanced transformer-based models. Notably, the findings of this work highlight the effectiveness of the Malayalam-BERT model, demonstrating an impressive macro F1 score of 0.88 in distinguishing between fake and original news in Malayalam social media content, achieving a commendable rank of 1st among the participants.
%U https://aclanthology.org/2024.dravidianlangtech-1.37
%P 223-228
Markdown (Informal)
[CUET_DUO@DravidianLangTech EACL2024: Fake News Classification Using Malayalam-BERT](https://aclanthology.org/2024.dravidianlangtech-1.37) (Rahman et al., DravidianLangTech-WS 2024)
ACL
- Tanzim Rahman, Abu Raihan, Md. Rahman, Jawad Hossain, Shawly Ahsan, Avishek Das, and Mohammed Moshiul Hoque. 2024. CUET_DUO@DravidianLangTech EACL2024: Fake News Classification Using Malayalam-BERT. In Proceedings of the Fourth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages, pages 223–228, St. Julian's, Malta. Association for Computational Linguistics.