Social Media Fake News Classification Using Machine Learning Algorithm

Girma Bade, Olga Kolesnikova, Grigori Sidorov, José Oropeza


Abstract
The rise of social media has facilitated easier communication, information sharing, and current affairs updates. However, the prevalence of misleading and deceptive content, commonly referred to as fake news, poses a significant challenge. This paper focuses on the classification of fake news in Malayalam, a Dravidian language, utilizing natural language processing (NLP) techniques. To develop a model, we employed a random forest machine learning method on a dataset provided by a shared task(DravidianLangTech@EACL 2024)1. When evaluated by the separate test dataset, our developed model achieved a 0.71 macro F1 measure.
Anthology ID:
2024.dravidianlangtech-1.4
Volume:
Proceedings of the Fourth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages
Month:
March
Year:
2024
Address:
St. Julian's, Malta
Editors:
Bharathi Raja Chakravarthi, Ruba Priyadharshini, Anand Kumar Madasamy, Sajeetha Thavareesan, Elizabeth Sherly, Rajeswari Nadarajan, Manikandan Ravikiran
Venues:
DravidianLangTech | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
24–29
Language:
URL:
https://aclanthology.org/2024.dravidianlangtech-1.4
DOI:
Bibkey:
Cite (ACL):
Girma Bade, Olga Kolesnikova, Grigori Sidorov, and José Oropeza. 2024. Social Media Fake News Classification Using Machine Learning Algorithm. In Proceedings of the Fourth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages, pages 24–29, St. Julian's, Malta. Association for Computational Linguistics.
Cite (Informal):
Social Media Fake News Classification Using Machine Learning Algorithm (Bade et al., DravidianLangTech-WS 2024)
Copy Citation:
PDF:
https://aclanthology.org/2024.dravidianlangtech-1.4.pdf
Video:
 https://aclanthology.org/2024.dravidianlangtech-1.4.mp4