BanglaMamba: Exploring State Space Models for Bangla Fake News Detection

Researchers proposed BanglaMamba, a Mamba-based State Space Model for Bangla fake news detection, and found it achieves a Macro-F1 score of 0.9029, comparable to a from-scratch CustomBERT (0.9057) but below BanglaBERT's 0.9260. BanglaMamba delivers approximately 2.2x higher inference throughput and 49% lower inference peak GPU memory usage than BERT-based models, offering a computationally efficient alternative for resource-constrained settings.

arXiv:2608.25190v1 Announce Type: new Abstract: 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.