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This study explores the use of Mamba-based State Space Models (SSMs) for detecting fake news in Bangla, addressing the limitations of transformer models like BanglaBERT, which suffer from high computational complexity. The proposed BanglaMamba model achieves a Macro-F1 score of 0.9029, showing comparable performance to a custom-trained BERT model while significantly improving inference throughput by approximately 2.2 times and reducing peak GPU memory usage by 49%. These results suggest that SSMs can serve as an efficient alternative for fake news detection in resource-constrained environments, although BanglaBERT remains superior in generalization to external datasets.
Mamba-based models can rival state-of-the-art transformers in fake news detection while slashing resource demands by nearly half.
Fake news detection has become an important Natural Language Processing (NLP) task due to the rapid spread of misinformation through online news platforms and social media. While transformer-based models such as BanglaBERT achieve strong performance for Bangla text classification, their quadratic computational complexity makes them less suitable for long-document processing in resource-constrained environments. This paper investigates Mamba-based State Space Models (SSMs) as an efficient alternative for Bangla fake news detection. We propose BanglaMamba and compare it with pre-trained BanglaBERT and a similarly configured BERT model trained from scratch. Experimental results show that BanglaBERT achieves the highest Macro-F1 score (0.9260), while BanglaMamba (0.9029) achieves performance comparable to the from-scratch CustomBERT (0.9057) despite using a different architecture. Meanwhile, BanglaMamba achieves approximately $2.2\times$ higher inference throughput and 49% lower inference peak GPU memory usage than the BERT-based models. Cross-dataset evaluation shows that BanglaBERT generalizes better to an external dataset, highlighting the importance of large-scale pretraining. These findings demonstrate that Mamba-based SSMs can provide a competitive and computationally efficient alternative to Transformer-based architectures for Bangla fake news detection, particularly in resource-constrained settings.
