Abstract
Nowadays, fake news is spreading rapidly. Many resources are available for fake news detection in high-resource languages like English. Due to the lack of annotated data and corpora for low-resource languages, detecting fake news in low-resource languages is difficult. There is a need for a system for fake news detection in low-resource languages like Dravidian languages. In this research, we used Telugu, Kannada, Tamil, and Malayalam languages and tested with four transformer models: mBERT, XLM-RoBERTa, IndicBERT, and MuRIL. MuRIL gives the best accuracy in these models compared to the remaining models.
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References
Shu K, Mahudeswaran D, Wang S, Lee D, Liu H (2020) FakeNewsNet: a data repository with news content, social context, and spatiotemporal information for studying fake news on social media. Big Data 8:171–188. https://doi.org/10.1089/big.2020.0062
Horne B, Adali S (2017) This just in: Fake news packs a lot in title, uses simpler, repetitive content in text body, more similar to satire than real news. In: Proceedings of the international AAAI conference on web and social media (2017)
Przybyla P (2020) Capturing the style of fake news. In: Proceedings of AAAI conference on artificial intellegence, vol 34, pp 490–497. https://doi.org/10.1609/aaai.v34i01.5386
Zellers R, Holtzman A, Rashkin H, Bisk Y, Farhadi A, Roesner F, Choi Y (2020) Defending against neural fake news. Neurips
Silva RM, Santos RLS, Almeida TA, Pardo TAS (2020) Towards automatically filtering fake news in Portuguese. Expert Syst Appl 146:113199. https://doi.org/10.1016/j.eswa.2020.113199
Potthast M, Kiesel J, Reinartz K, Bevendorff J, Stein B (2017) A stylometric inquiry into hyperpartisan and fake news. arXiv Preprint http://arxiv.org/abs/1702.05638
Nguyen VH, Sugiyama K, Nakov P, Kan MY (2020) FANG: leveraging social context for fake news detection using graph representation. In: International conference on information & knowledge management. Proceedings. https://doi.org/10.1145/3340531.3412046
Devlin J, Chang MW, Lee K, Toutanova K (2018) BERT: pretraining of deep bidirectional transformers for language understanding. arXiv Preprint http://arxiv.org/abs/1810.04805
Wu X, Lode M (2020) Language models are unsupervised multitask learners (summarization). OpenAI Blog 1:1–7
Liu C, Wu X, Yu M, Li G, Jiang J, Huang W, Lu X (2019) A two-stage model based on BERT for short fake news detection. In: Lecture notes in computer science (Including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics, vol 11776 LNAI, pp 172–183. https://doi.org/10.1007/978-3-030-29563-9_17
Jwa H, Oh D, Park K, Kang JM, Lim H (2019) exBAKE: automatic fake news detection model based on bidirectional encoder representations from transformers (BERT). Appl Sci 9:4062. https://doi.org/10.3390/app9194062
Vijjali R, Potluri P, Kumar S, Teki S (2020) Two stage transformer model for covid-19 fake news detection and fact checking. arXiv Preprint http://arxiv.org/abs/2011.13253
Kakwani D, Kunchukuttan A, Golla S, Gokul NC, Bhattacharyya A, Khapra MM, Kumar P (2020) IndicNLPSuite: Monolingual Corpora, evaluation benchmarks and pre-trained multilingual language models for Indian languages. In: Findings of EMNLP
Devlin J, Chang M-W, Lee K, Toutanova K (2019) Bert: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805
Khanuja S, Bansal D, Mehtani S, Khosla S, Dey A, Gopalan B, Margam DK, Aggarwal P, Nagipogu RT, Dave S, Gupta S, Gali SCB, Subramanian V, Talukdar P (2021) Muril: multilingual representations for Indian languages. arXiv:2103.10730
Conneau A, Khandelwal K, Goyal N, Chaudhary V, Wenzek G, Guzman F, Grave E, Ott M, Zettle-moyer L, Stoyanov V (2019) Unsupervised cross-lingual representation learning at scale. arXiv preprint arXiv:1911.02116
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Raja, E., Soni, B., Borgohain, S.K. (2024). Fake News Detection in Dravidian Languages Using Transformer Models. In: Malhotra, R., Sumalatha, L., Yassin, S.M.W., Patgiri, R., Muppalaneni, N.B. (eds) High Performance Computing, Smart Devices and Networks. CHSN 2022. Lecture Notes in Electrical Engineering, vol 1087. Springer, Singapore. https://doi.org/10.1007/978-981-99-6690-5_39
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DOI: https://doi.org/10.1007/978-981-99-6690-5_39
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