BanglaFakeNews: A Curated Dataset for Bengali Fake News Detection

BanglaFakeNews is a large-scale, curated dataset developed for fake news detection in the Bengali language. The dataset contains 50,800 labeled Bengali news articles collected from multiple publicly available online news sources and verified fact-checking platforms. Each news article is manually assigned one of two labels: Fake or Authentic, enabling binary classification for misinformation detection. The dataset was created to address the scarcity of high-quality benchmark datasets for Bengali fake news research. It covers a diverse range of topics, including politics, health, entertainment, sports, technology, crime, education, business, and social issues, making it suitable for developing robust and generalizable machine learning and deep learning models. The text data have undergone preprocessing to improve consistency and usability. The preprocessing pipeline includes duplicate removal, text normalization, tokenization, stop-word removal, and stemming while preserving the semantic content of the original news articles. The dataset is provided in CSV format and is intended to facilitate reproducible research in natural language processing (NLP), misinformation detection, text classification, and artificial intelligence applications for low-resource languages. Researchers can use this dataset to develop, evaluate, and benchmark traditional machine learning models, transformer-based architectures, and hybrid deep learning frameworks for Bengali fake news detection. It may also support related research in sentiment analysis, misinformation analysis, domain adaptation, explainable AI, and multilingual NLP. If this dataset contributes to your research, please cite the associated publication: A Hybrid Deep Learning Framework for Fake News Detection in Bengali News.