# Approaches for Improving the Performance of Fake News Detection in Bangla: Imbalance Handling and Model Stacking

arXiv: https://arxiv.org/abs/2203.11486 · HTML: https://mlsanity.com/paper/2203.11486/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 14 references that are in the map.
- First posted on arXiv 22 Mar 2022.

## Builds on (most cited first, 5 of 5)

- ["Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection](https://mlsanity.com/paper/1705.00648/) (2017)
- [Online Human-Bot Interactions: Detection, Estimation, and Characterization](https://mlsanity.com/paper/1703.03107/) (2017)
- [r/Fakeddit: A New Multimodal Benchmark Dataset for Fine-grained Fake News Detection](https://mlsanity.com/paper/1911.03854/) (2019)
- [BanFakeNews: A Dataset for Detecting Fake News in Bangla](https://mlsanity.com/paper/2004.08789/) (2020)
- [Detection of Bangla Fake News using MNB and SVM Classifier](https://mlsanity.com/paper/2005.14627/) (2020)

## Built on by (most cited first, 2 of 2)

- [Enhancing Bangla Fake News Detection Using Bidirectional Gated Recurrent Units and Deep Learning Techniques](https://mlsanity.com/paper/2404.01345/) (2024)
- [Breaking the Fake News Barrier: Deep Learning Approaches in Bangla Language](https://mlsanity.com/paper/2501.18766/) (2025)

## Abstract (arXiv)

Imbalanced datasets can lead to biasedness into the detection of fake news. In this work, we present several strategies for resolving the imbalance issue for fake news detection in Bangla with a comparative assessment of proposed methodologies. Additionally, we propose a technique for improving performance even when the dataset is imbalanced. We applied our proposed approaches to BanFakeNews, a dataset developed for the purpose of detecting fake news in Bangla comprising of 50K instances but is significantly skewed, with 97% of majority instances. We obtained a 93.1% F1-score using data manipulation manipulation techniques such as SMOTE, and a 79.1% F1-score using without data manipulation approaches such as Stacked Generalization. Without implementing these techniques, the F1-score would have been 67.6% for baseline models. We see this work as an important step towards paving the way of fake news detection in Bangla. By implementing these strategies the obstacles of imbalanced dataset can be removed and improvement in the performance can be achieved.