@inproceedings{subramanian-etal-2024-kec,
title = "{KEC}{\_}{HAWKS}@{D}ravidian{L}ang{T}ech 2024 : Detecting {M}alayalam Fake News using Machine Learning Models",
author = "Subramanian, Malliga and
J R, Jayanthjr and
Karuppan P, Muthu and
T, Keerthibala and
Shanmugavadivel, Kogilavani",
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-1.45",
pages = "266--270",
abstract = "The proliferation of fake news in the Malayalam language across digital platforms has emerged as a pressing issue. By employing Recurrent Neural Networks (RNNs), a type of machine learning model, we aim to distinguish between Original and Fake News in Malayalam and achieved 9th rank in Task 1.RNNs are chosen for their ability to understand the sequence of words in a sentence, which is important in languages like Malayalam. Our main goal is to develop better models that can spot fake news effectively. We analyze various features to understand what contributes most to this accuracy. By doing so, we hope to provide a reliable method for identifying and combating fake news in the Malayalam language.",
}
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<abstract>The proliferation of fake news in the Malayalam language across digital platforms has emerged as a pressing issue. By employing Recurrent Neural Networks (RNNs), a type of machine learning model, we aim to distinguish between Original and Fake News in Malayalam and achieved 9th rank in Task 1.RNNs are chosen for their ability to understand the sequence of words in a sentence, which is important in languages like Malayalam. Our main goal is to develop better models that can spot fake news effectively. We analyze various features to understand what contributes most to this accuracy. By doing so, we hope to provide a reliable method for identifying and combating fake news in the Malayalam language.</abstract>
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%0 Conference Proceedings
%T KEC_HAWKS@DravidianLangTech 2024 : Detecting Malayalam Fake News using Machine Learning Models
%A Subramanian, Malliga
%A J R, Jayanthjr
%A Karuppan P, Muthu
%A T, Keerthibala
%A Shanmugavadivel, Kogilavani
%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 subramanian-etal-2024-kec
%X The proliferation of fake news in the Malayalam language across digital platforms has emerged as a pressing issue. By employing Recurrent Neural Networks (RNNs), a type of machine learning model, we aim to distinguish between Original and Fake News in Malayalam and achieved 9th rank in Task 1.RNNs are chosen for their ability to understand the sequence of words in a sentence, which is important in languages like Malayalam. Our main goal is to develop better models that can spot fake news effectively. We analyze various features to understand what contributes most to this accuracy. By doing so, we hope to provide a reliable method for identifying and combating fake news in the Malayalam language.
%U https://aclanthology.org/2024.dravidianlangtech-1.45
%P 266-270
Markdown (Informal)
[KEC_HAWKS@DravidianLangTech 2024 : Detecting Malayalam Fake News using Machine Learning Models](https://aclanthology.org/2024.dravidianlangtech-1.45) (Subramanian et al., DravidianLangTech-WS 2024)
ACL