@inproceedings{raja-etal-2023-nlpt,
title = "nlpt malayalm@{D}ravidian{L}ang{T}ech : Fake News Detection in {M}alayalam using Optimized {XLM}-{R}o{BERT}a Model",
author = "Raja, Eduri and
Soni, Badal and
Borgohain, Sami Kumar",
editor = "Chakravarthi, Bharathi R. and
Priyadharshini, Ruba and
M, Anand Kumar and
Thavareesan, Sajeetha and
Sherly, Elizabeth",
booktitle = "Proceedings of the Third Workshop on Speech and Language Technologies for Dravidian Languages",
month = sep,
year = "2023",
address = "Varna, Bulgaria",
publisher = "INCOMA Ltd., Shoumen, Bulgaria",
url = "https://aclanthology.org/2023.dravidianlangtech-1.26",
pages = "186--191",
abstract = "The paper demonstrates the submission of the team nlpt{\_}malayalm to the Fake News Detection in Dravidian Languages-DravidianLangTech@LT-EDI-2023. The rapid dissemination of fake news and misinformation in today{'}s digital age poses significant societal challenges. This research paper addresses the issue of fake news detection in the Malayalam language by proposing a novel approach based on the XLM-RoBERTa base model. The objective is to develop an effective classification model that accurately differentiates between genuine and fake news articles in Malayalam. The XLM-RoBERTa base model, known for its multilingual capabilities, is fine-tuned using the prepared dataset to adapt it specifically to the nuances of the Malayalam language. A thorough analysis is also performed to identify any biases or limitations in the model{'}s performance. The results demonstrate that the proposed model achieves a remarkable macro-averaged F-Score of 87{\%} in the Malayalam fake news dataset, ranking 2nd on the respective task. This indicates its high accuracy and reliability in distinguishing between real and fake news in Malayalam.",
}
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<abstract>The paper demonstrates the submission of the team nlpt_malayalm to the Fake News Detection in Dravidian Languages-DravidianLangTech@LT-EDI-2023. The rapid dissemination of fake news and misinformation in today’s digital age poses significant societal challenges. This research paper addresses the issue of fake news detection in the Malayalam language by proposing a novel approach based on the XLM-RoBERTa base model. The objective is to develop an effective classification model that accurately differentiates between genuine and fake news articles in Malayalam. The XLM-RoBERTa base model, known for its multilingual capabilities, is fine-tuned using the prepared dataset to adapt it specifically to the nuances of the Malayalam language. A thorough analysis is also performed to identify any biases or limitations in the model’s performance. The results demonstrate that the proposed model achieves a remarkable macro-averaged F-Score of 87% in the Malayalam fake news dataset, ranking 2nd on the respective task. This indicates its high accuracy and reliability in distinguishing between real and fake news in Malayalam.</abstract>
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%0 Conference Proceedings
%T nlpt malayalm@DravidianLangTech : Fake News Detection in Malayalam using Optimized XLM-RoBERTa Model
%A Raja, Eduri
%A Soni, Badal
%A Borgohain, Sami Kumar
%Y Chakravarthi, Bharathi R.
%Y Priyadharshini, Ruba
%Y M, Anand Kumar
%Y Thavareesan, Sajeetha
%Y Sherly, Elizabeth
%S Proceedings of the Third Workshop on Speech and Language Technologies for Dravidian Languages
%D 2023
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F raja-etal-2023-nlpt
%X The paper demonstrates the submission of the team nlpt_malayalm to the Fake News Detection in Dravidian Languages-DravidianLangTech@LT-EDI-2023. The rapid dissemination of fake news and misinformation in today’s digital age poses significant societal challenges. This research paper addresses the issue of fake news detection in the Malayalam language by proposing a novel approach based on the XLM-RoBERTa base model. The objective is to develop an effective classification model that accurately differentiates between genuine and fake news articles in Malayalam. The XLM-RoBERTa base model, known for its multilingual capabilities, is fine-tuned using the prepared dataset to adapt it specifically to the nuances of the Malayalam language. A thorough analysis is also performed to identify any biases or limitations in the model’s performance. The results demonstrate that the proposed model achieves a remarkable macro-averaged F-Score of 87% in the Malayalam fake news dataset, ranking 2nd on the respective task. This indicates its high accuracy and reliability in distinguishing between real and fake news in Malayalam.
%U https://aclanthology.org/2023.dravidianlangtech-1.26
%P 186-191
Markdown (Informal)
[nlpt malayalm@DravidianLangTech : Fake News Detection in Malayalam using Optimized XLM-RoBERTa Model](https://aclanthology.org/2023.dravidianlangtech-1.26) (Raja et al., DravidianLangTech-WS 2023)
ACL