Bangla BERT for Hyperpartisan News Detection: A Semi-Supervised and Explainable AI Approach

TL;DR

This paper introduces Bangla BERT, a semi-supervised, explainable transformer model that significantly improves hyperpartisan news detection accuracy in low-resource Bangla language environments.

Contribution

It fine-tunes Bangla BERT for hyperpartisan news detection, incorporates semi-supervised learning, and uses LIME for explainability, advancing NLP methods for low-resource languages.

Findings

Achieved 95.65% accuracy in hyperpartisan news detection.

Outperformed traditional machine learning models.

Demonstrated effectiveness of transformer models in low-resource settings.

Abstract

In the current digital landscape, misinformation circulates rapidly, shaping public perception and causing societal divisions. It is difficult to identify hyperpartisan news in Bangla since there aren't many sophisticated natural language processing methods available for this low-resource language. Without effective detection methods, biased content can spread unchecked, posing serious risks to informed discourse. To address this gap, our research fine-tunes Bangla BERT. This is a state-of-the-art transformer-based model, designed to enhance classification accuracy for hyperpartisan news. We evaluate its performance against traditional machine learning models and implement semi-supervised learning to enhance predictions further. Not only that, we use LIME to provide transparent explanations of the model's decision-making process, which helps to build trust in its outcomes. With a remarkable accuracy score of 95.65%, Bangla BERT outperforms conventional approaches, according to our trial data. The findings of this study demonstrate the usefulness of transformer models even in environments with limited resources, which opens the door to further improvements in this area.