  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.
