Misinformation Detection: A Review for High and Low-Resource Languages

  • Seani Rananga North-West University and University of Pretoria, South Africa
  • Bassey Isong North-West University, South Africa
  • Abiodun Modupe University of Pretoria, South Africa
  • Vukosi Marivate University of Pretoria, South Africa
Keywords: Misinformation Detection, Low-Resource Languages, High-Resource Languages, African Languages.

Abstract

The rapid spread of misinformation on platforms like Twitter, and Facebook, and in news headlines highlights the urgent need for effective ways to detect it. Currently, researchers are increasingly using machine learning (ML) and deep learning (DL) techniques to tackle misinformation detection (MID) because of their proven success. However, this task is still challenging due to the complexity of deceptive language, digital editing tools, and the lack of reliable linguistic resources for non-English languages. This paper provides a comprehensive analysis of relevant research, providing insights into advanced techniques for MID. It covers dataset assessments, the importance of using multiple forms of data (multimodality), and different language representations. By applying the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) methodology, the study identified and analyzed literature from 2019 to 2024 across five databases: Google Scholar, Springer, Elsevier, ACM, and IEEE Xplore. The study selected thirty-one papers and examined the effectiveness of various ML and DL approaches with a focal point on performance metrics, datasets, and false or misleading information detection challenges. The findings indicate that most current MID models are heavily dependent on DL techniques, with approximately 81% of studies preferring these over traditional ML methods. In addition, most studies are text-based, with much less attention given to audio, speech, images, and videos. The most effective models are mainly designed for high-resource languages, with English datasets being the most used (67%), followed by Arabic (14%), Chinese (11%), and others. Less than 10% of the studies focus on low-resource languages (LRLs). Therefore, the study highlighted the need for robust datasets and interpretable, scalable MID models for LRLs. It emphasizes the critical need to prioritize and advance MID research for LRLs across all data types, including text, audio, speech, images, videos, and multimodal approaches.  This study aims to support ongoing efforts to combat misinformation and promote a more informed understanding of under-resourced African languages.

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References

De, A., Bandyopadhyay, D., Gain, B., and Ekbal, A.: ‘A transformer-based approach to multilingual fake news detection in low-resource languages’, Transactions on Asian and Low-Resource Language Information Processing, 2021, 21, (1), pp. 1-20

Farhangian, F., Cruz, R.M., and Cavalcanti, G.D.: ‘Fake news detection: Taxonomy and comparative study’, Information Fusion, 2024, 103, pp. 102140

Alghamdi, J., Lin, Y., and Luo, S.: ‘Fake news detection in low-resource languages: A novel hybrid summarization approach’, Knowledge-Based Systems, 2024, 296, pp. 111884

Nakamura, K., Levy, S., and Wang, W.Y.: ‘r/fakeddit: A new multimodal benchmark dataset for fine-grained fake news detection’, arXiv preprint arXiv:1911.03854, 2019

Hosseini, M., Sabet, A.J., He, S., and Aguiar, D.: ‘Interpretable fake news detection with topic and deep variational models’, Online Social Networks and Media, 2023, 36, pp. 100249

Salau, A.O., Arega, K.L., Tin, T.T., Quansah, A., Sefa-Boateng, K., Chowdhury, I.J., and Braide, S.L.: ‘Machine learning-based detection of fake news in Afan Oromo language’, Bulletin of Electrical Engineering and Informatics, 2024, 13, (6), pp. 4260-4272

Zeng, F., Li, W., Gao, W., and Pang, Y.: ‘Multimodal Misinformation Detection by Learning from Synthetic Data with Multimodal LLMs’, arXiv preprint arXiv:2409.19656, 2024

Gereme, F.B., and Zhu, W.: ‘Early detection of fake news" before it flies high"’, in Editor (Ed.)^(Eds.): ‘Book Early detection of fake news" before it flies high"’ (2019, edn.), pp. 142-148

Rashid, M.R.A., Roy, R., Rahman, D.M.S., Saleh, M.A., Khan, A.A.H., Rayhan, A., Ahmed, K.F., Monsoor, N., and Hasan, M.: ‘A Comprehensive Dataset and Deep Learning Approach for Misinformation Detection on Social Media in Bangladesh’, International Journal of Computing and Digital Systems, 2024, 16, (1), pp. 1-10

Raja, E., Soni, B., Lalrempuii, C., and Borgohain, S.K.: ‘An adaptive cyclical learning rate based hybrid model for Dravidian fake news detection’, Expert Systems with Applications, 2024, 241, pp. 122768

Al-Zahrani, L., and Al-Yahya, M.: ‘Pre-Trained Language Model Ensemble for Arabic Fake News Detection’, Mathematics, 2024, 12, (18), pp. 1-17

Hashmi, E., Yayilgan, S.Y., Yamin, M.M., Ali, S., and Abomhara, M.: ‘Advancing fake news detection: hybrid deep learning with fast text and explainable AI’, IEEE Access, 2024

Hossain, M.R., Hoque, M.M., Siddique, N., and Dewan, M.A.A.: ‘AraCovTexFinder: Leveraging the transformer-based language model for Arabic COVID-19 text identification’, Engineering Applications of Artificial Intelligence, 2024, 133, pp. 107987

Lin, H., Ma, J., Yang, R., Yang, Z., and Cheng, M.: ‘Towards low-resource rumour detection: Unified contrastive transfer with propagation structure’, Neurocomputing, 2024, 578, pp. 127438

Mallik, A., and Kumar, S.: ‘Word2Vec and LSTM based deep learning technique for context-free fake news detection’, Multimedia Tools and Applications, 2024, 83, (1), pp. 919-940

Mohsen, F., Chaushi, B., Abdelhaq, H., Karastoyanova, D., and Wang, K.: ‘Automated Detection of Misinformation: A Hybrid Approach for Fake News Detection’, Future Internet, 2024, 16, (10), pp. 352

Al-Alshaqi, M., Rawat, D.B., and Liu, C.: ‘Ensemble Techniques for Robust Fake News Detection: Integrating Transformers, Natural Language Processing, and Machine Learning’, Sensors, 2024, 24, (18), pp. 6062

Ricketts, J.A.: ‘Powers-of-ten information biases’, MIS Quarterly, 1990, pp. 63-77

19 Praseed, A., Rodrigues, J., and Thilagam, P.S.: ‘Hindi fake news detection using transformer ensembles’, Engineering Applications of Artificial Intelligence, 2023, 119, pp. 105731

Su, Q., Wan, M., Liu, X., and Huang, C.-R.: ‘Motivations, methods and metrics of misinformation detection: an NLP perspective’, Natural Language Processing Research, 2020, 1, (1-2), pp. 1-13

Verma, P.K., Agrawal, P., Madaan, V., and Prodan, R.: ‘MCred: multi-modal message credibility for fake news detection using BERT and CNN’, Journal of Ambient Intelligence and Humanized Computing, 2023, 14, (8), pp. 10617-10629

Luvembe, A.M., Li, W., Li, S., Liu, F., and Wu, X.: ‘CAF-ODNN: Complementary attention fusion with optimized deep neural network for multimodal fake news detection’, Information Processing & Management, 2024, 61, (3), pp. 103653

Kumar, A., Esposito, C., and Karras, D.A.: ‘Introduction to special issue on misinformation, fake news and rumour detection in low-resource languages’, in Editor (Ed.)^(Eds.): ‘Book Introduction to special issue on misinformation, fake news and rumour detection in low-resource languages’ (ACM New York, NY, 2021, edn.), pp. 1-3

Yu, C., Han, J., Zhang, H., and Ng, W.: ‘Hypernymy detection for low-resource languages via meta learning’, in Editor (Ed.)^(Eds.): ‘Book Hypernymy detection for low-resource languages via meta learning’ (2020, edn.), pp. 3651-3656

Kar, D., Bhardwaj, M., Samanta, S., and Azad, A.P.: ‘No rumours please! A multi-indic-lingual approach for COVID fake-tweet detection’, in Editor (Ed.)^(Eds.): ‘Book No rumours, please! A multi-indic-lingual approach for COVID fake-tweet detection’ (IEEE, 2021, edn.), pp. 1-5

Ghafoor, A., Imran, A.S., Daudpota, S.M., Kastrati, Z., Batra, R., and Wani, M.A.: ‘The impact of translating resource-rich datasets to low-resource languages through multi-lingual text processing’, IEEE Access, 2021, 9, pp. 124478-124490

Gereme, F., Zhu, W., Ayall, T., and Alemu, D.: ‘Combating fake news in “low-resource” languages: Amharic fake news detection accompanied by resource crafting’, Information, 2021, 12, (1), pp. 20

Du, J., Dou, Y., Xia, C., Cui, L., Ma, J., and Philip, S.Y.: ‘Cross-lingual covid-19 fake news detection’, in Editor (Ed.)^(Eds.): ‘Book Cross-lingual covid-19 fake news detection’ (IEEE, 2021, edn.), pp. 859-862

Kim, J., Bak, B., Agrawal, A., Wu, J., Wirtz, V., Hong, T., and Wijaya, D.: ‘Covid-19 vaccine misinformation in middle income countries’, in Editor (Ed.)^(Eds.): ‘Book Covid-19 vaccine misinformation in middle income countries’ (Association for Computational Linguistics, 2023, edn.), pp.

Kuznetsova, E., Makhortykh, M., Vziatysheva, V., Stolze, M., Baghumyan, A., and Urman, A.: ‘In Generative AI we Trust: Can Chatbots Effectively Verify Political Information?’, arXiv preprint arXiv:2312.13096, 2023

Moher, D., Liberati, A., Tetzlaff, J., Altman, D.G., and PRISMA Group*, t.: ‘Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement’, Annals of internal medicine, 2009, 151, (4), pp. 264-269

Yan, F., Zhang, M., Wei, B., Ren, K., and Jiang, W.: ‘FMC: Multimodal fake news detection based on multi-granularity feature fusion and contrastive learning’, Alexandria Engineering Journal, 2024, 109, pp. 376-393

Albalawi, R.M., Jamal, A.T., Khadidos, A.O., and Alhothali, A.M.: ‘Multimodal Arabic rumours detection’, IEEE Access, 2023, 11, pp. 9716-9730

Zheng, P., Chen, H., Hu, S., Zhu, B., Hu, J., Lin, C.-S., Wu, X., Lyu, S., Huang, G., and Wang, X.: ‘Few-shot learning for misinformation detection based on contrastive models’, Electronics, 2024, 13, (4), pp. 799

Van der Westhuizen, E., Kamper, H., Menon, R., Quinn, J., and Niesler, T.: ‘Feature learning for efficient ASR-free keyword spotting in low-resource languages’, Computer Speech & Language, 2022, 71, pp. 101275

Hansrajh, A., Adeliyi, T.T., and Wing, J.: ‘Detection of online fake news using blending ensemble learning’, Scientific Programming, 2021, 2021, (1), pp. 3434458

Reis, J.C., Correia, A., Murai, F., Veloso, A., and Benevenuto, F.: ‘Supervised learning for fake news detection’, IEEE Intelligent Systems, 2019, 34, (2), pp. 76-81

Asghar, M.Z., Habib, A., Habib, A., Khan, A., Ali, R., and Khattak, A.: ‘Exploring deep neural networks for rumour detection’, Journal of Ambient Intelligence and Humanized Computing, 2021, 12, pp. 4315-4333

Jadhav, S.S., and Thepade, S.D.: ‘Fake news identification and classification using DSSM and improved recurrent neural network classifier’, Applied Artificial Intelligence, 2019, 33, (12), pp. 1058-1068

Kaliyar, R.K., Goswami, A., and Narang, P.: ‘DeepFakE: improving fake news detection using tensor decomposition-based deep neural network’, The Journal of Supercomputing, 2021, 77, (2), pp. 1015-1037

Lin, H., Yi, P., Ma, J., Jiang, H., Luo, Z., Shi, S., and Liu, R.: ‘Zero-shot rumour detection with propagation structure via prompt learning’, in Editor (Ed.)^(Eds.): ‘Book Zero-shot rumour detection with propagation structure via prompt learning’ (2023, edn.), pp. 5213-5221

Guo, Z., Zhang, Q., Ding, F., Zhu, X., and Yu, K.: ‘A novel fake news detection model for the context of mixed languages through multiscale transformer’, IEEE Transactions on Computational Social Systems, 2023

Dlamini, G., Bekkouch, I.E.I., Khan, A., and Derczynski, L.: ‘Bridging the domain gap for stance detection for the Zulu language’, in Editor (Ed.)^(Eds.): ‘Book Bridging the domain gap for stance detection for the Zulu language’ (Springer, 2022, edn.), pp. 312-325

De Wet, H., and Marivate, V.: ‘Is it fake? News disinformation detection on South African news websites, in Editor (Ed.)^(Eds.): ‘Book Is it fake? News disinformation detection on South African news websites’ (IEEE, 2021, edn.), pp. 1-6

Published
2024-12-31
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How to Cite
Rananga, S., Isong, B., Modupe, A., & Marivate, V. (2024). Misinformation Detection: A Review for High and Low-Resource Languages. Journal of Information Systems and Informatics, 6(4), 2892-2922. https://doi.org/10.51519/journalisi.v6i4.931
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Articles