{"slug": "banglamamba-exploring-state-space-models-for-bangla-fake-news-detection", "title": "BanglaMamba: Exploring State Space Models for Bangla Fake News Detection", "summary": "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.", "body_md": "arXiv:2608.25190v1 Announce Type: new\nAbstract: 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.", "url": "https://wpnews.pro/news/banglamamba-exploring-state-space-models-for-bangla-fake-news-detection", "canonical_source": "https://arxiv.org/abs/2608.25190", "published_at": "2026-08-27 04:00:00+00:00", "updated_at": "2026-08-27 04:20:25.734661+00:00", "lang": "en", "topics": ["artificial-intelligence", "natural-language-processing", "machine-learning"], "entities": ["BanglaMamba", "BanglaBERT", "CustomBERT", "Mamba", "State Space Models"], "alternates": {"html": "https://wpnews.pro/news/banglamamba-exploring-state-space-models-for-bangla-fake-news-detection", "markdown": "https://wpnews.pro/news/banglamamba-exploring-state-space-models-for-bangla-fake-news-detection.md", "text": "https://wpnews.pro/news/banglamamba-exploring-state-space-models-for-bangla-fake-news-detection.txt", "jsonld": "https://wpnews.pro/news/banglamamba-exploring-state-space-models-for-bangla-fake-news-detection.jsonld"}}