@inproceedings{gowda-hegde-2025-yencs,
title = "{Y}en{CS}@{D}ravidian{L}ang{T}ech 2025: Integrating Hybrid Architectures for Fake News Detection in Low-Resource {D}ravidian Languages",
author = "Gowda, Anusha M D and
Hegde, Parameshwar R",
editor = "Chakravarthi, Bharathi Raja and
Priyadharshini, Ruba and
Madasamy, Anand Kumar and
Thavareesan, Sajeetha and
Sherly, Elizabeth and
Rajiakodi, Saranya and
Palani, Balasubramanian and
Subramanian, Malliga and
Cn, Subalalitha and
Chinnappa, Dhivya",
booktitle = "Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages",
month = may,
year = "2025",
address = "Acoma, The Albuquerque Convention Center, Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.dravidianlangtech-1.19/",
doi = "10.18653/v1/2025.dravidianlangtech-1.19",
pages = "109--113",
ISBN = "979-8-89176-228-2",
abstract = "Detecting fake news in under-resourced Dravidian languages is a rigorous task due to the scarcity of annotated datasets and the intricate nature of code-mixed text. This study tackles these issues by employing advanced machine learning techniques for two key classification tasks, the first task involves binary classification achieving a macro-average F1-score of 0.792 using a hybrid fusion model that integrates Bidirectional Recurrent Neural Network (Bi-RNN) and Long Short-Term Memory (LSTM)-Recurrent Neural Network (RNN) with weighted averaging. The second task focuses on fine-grained classification, categorizing news where an LSTM-GRU hybrid model attained a macro-average F1-score of 0.26. These findings highlight the effectiveness of hybrid models in improving fake news detection for under-resourced languages. Additionally, this study provides a foundational framework that can be adapted to address similar challenges in other under-resourced languages, emphasizing the need for further research in this area."
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%0 Conference Proceedings
%T YenCS@DravidianLangTech 2025: Integrating Hybrid Architectures for Fake News Detection in Low-Resource Dravidian Languages
%A Gowda, Anusha M. D.
%A Hegde, Parameshwar R.
%Y Chakravarthi, Bharathi Raja
%Y Priyadharshini, Ruba
%Y Madasamy, Anand Kumar
%Y Thavareesan, Sajeetha
%Y Sherly, Elizabeth
%Y Rajiakodi, Saranya
%Y Palani, Balasubramanian
%Y Subramanian, Malliga
%Y Cn, Subalalitha
%Y Chinnappa, Dhivya
%S Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages
%D 2025
%8 May
%I Association for Computational Linguistics
%C Acoma, The Albuquerque Convention Center, Albuquerque, New Mexico
%@ 979-8-89176-228-2
%F gowda-hegde-2025-yencs
%X Detecting fake news in under-resourced Dravidian languages is a rigorous task due to the scarcity of annotated datasets and the intricate nature of code-mixed text. This study tackles these issues by employing advanced machine learning techniques for two key classification tasks, the first task involves binary classification achieving a macro-average F1-score of 0.792 using a hybrid fusion model that integrates Bidirectional Recurrent Neural Network (Bi-RNN) and Long Short-Term Memory (LSTM)-Recurrent Neural Network (RNN) with weighted averaging. The second task focuses on fine-grained classification, categorizing news where an LSTM-GRU hybrid model attained a macro-average F1-score of 0.26. These findings highlight the effectiveness of hybrid models in improving fake news detection for under-resourced languages. Additionally, this study provides a foundational framework that can be adapted to address similar challenges in other under-resourced languages, emphasizing the need for further research in this area.
%R 10.18653/v1/2025.dravidianlangtech-1.19
%U https://aclanthology.org/2025.dravidianlangtech-1.19/
%U https://doi.org/10.18653/v1/2025.dravidianlangtech-1.19
%P 109-113
Markdown (Informal)
[YenCS@DravidianLangTech 2025: Integrating Hybrid Architectures for Fake News Detection in Low-Resource Dravidian Languages](https://aclanthology.org/2025.dravidianlangtech-1.19/) (Gowda & Hegde, DravidianLangTech 2025)
ACL