| Título |
Habesha@DravidianLangTech 2024: Detecting Fake News Detection in Dravidian Languages using Deep Learning |
| Tipo |
Congreso |
| Sub-tipo |
Memoria |
| Descripción |
4th Workshop on Speech, Vision, and Language Technologies for Dravidian Languages, DravidianLangTech 2024 |
| Resumen |
This research tackles the issue of fake news by utilizing the RNN-LSTM deep learning method with optimized hyperparameters identified through grid search. The model’s performance in multi-label classification is hindered by unbalanced data, despite its success in binary classification. We achieved a score of 0.82 in the binary classification task, whereas in the multi-class task, the score was 0.32. We suggest incorporating data balancing techniques for researchers who aim to further this task, aiming to improve results in managing a variety of information. © 2024 Association for Computational Linguistics. |
| Observaciones |
|
| Lugar |
St. Julians |
| País |
Malta |
| No. de páginas |
156-161 |
| Vol. / Cap. |
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| Inicio |
2024-01-01 |
| Fin |
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| ISBN/ISSN |
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