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BanFakeNews: A Dataset for Detecting Fake News in Bangla

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arxiv 2004.08789 v1 pith:NAJYQ3GR submitted 2020-04-19 cs.CL

Md Zobaer Hossain , Md Ashraful Rahman , Md Saiful Islam , Sudipta Kar This is my paper · ORCID
classification cs.CL
keywords newsfakedatasetresourcebanglalanguagesanalysisbuilding
verification ladder T0 review T1 audit T2 compute T3 formal
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Observing the damages that can be done by the rapid propagation of fake news in various sectors like politics and finance, automatic identification of fake news using linguistic analysis has drawn the attention of the research community. However, such methods are largely being developed for English where low resource languages remain out of the focus. But the risks spawned by fake and manipulative news are not confined by languages. In this work, we propose an annotated dataset of ~50K news that can be used for building automated fake news detection systems for a low resource language like Bangla. Additionally, we provide an analysis of the dataset and develop a benchmark system with state of the art NLP techniques to identify Bangla fake news. To create this system, we explore traditional linguistic features and neural network based methods. We expect this dataset will be a valuable resource for building technologies to prevent the spreading of fake news and contribute in research with low resource languages.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Hybrid AI for Responsive Multi-Turn Online Conversations with Novel Dynamic Routing and Feedback Adaptation

    cs.AI 2025-06 conditional novelty 4.0 of 10

    A hybrid chatbot that routes easy queries to canned responses and complex queries to RAG reports 95% accuracy and 180ms latency on an internal support dataset.

  2. Improving Bangla Linguistics: Advanced LSTM, Bi-LSTM, and Seq2Seq Models for Translating Sylheti to Modern Bangla

    cs.CL 2025-05 reject novelty 4.0 of 10

    The authors report that an LSTM model achieves 89.3% accuracy on a 1,200-sentence Sylheti-to-Modern Bangla translation task, but the evaluation protocol and dataset are not described rigorously enough to support the claim.

  3. A Comprehensive Survey on Imbalanced Data Learning

    cs.LG 2025-02 conditional novelty 3.0 of 10

    A structured survey and benchmark that groups imbalanced data learning methods into data re-balancing, feature representation, training strategy, and ensemble learning.

  4. Breaking the Fake News Barrier: Deep Learning Approaches in Bangla Language

    cs.CL 2025-01 reject novelty 2.0 of 10

    A standard GRU classifier is claimed to reach 94% accuracy on Bangla fake news detection, but the paper's data and baseline comparisons are internally inconsistent.

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