Hybrid Feature Combinations with CNN for Bangla Fake News Classification

TL;DR

This paper investigates the use of combined semantic, statistical, and character-level features with CNNs to improve Bangla fake news detection accuracy.

Contribution

It introduces a feature selection approach that enhances fake news classification by combining multiple feature types with CNNs on the BanFakeNews-2.0 dataset.

Findings

Combining features improves recall and F1-score.

Semantic and character features are particularly effective.

The proposed method outperforms individual feature-based models.

Abstract

Nowadays, people in Bangladesh frequently rely on the internet and social media for daily news instead of traditional newspapers. However, the spread of false Bangla news through these platforms poses risks and challenges to the credibility of authentic media. Although several studies have been conducted on detecting Bangla fake news, there is still significant room for improvement in this area. To assist people, this research explores the effectiveness of feature selection approaches in identifying appropriate features, such as semantic, statistical, and character-level features, or their combinations, on the BanFakeNews-2.0 dataset for detecting Bangla fake news using a CNN model. In this paper, key findings reveal that combining multiple features significantly improves recall and F1-scores compared to using individual features alone. The code for this research can be availed here, https://github.com/gulzar09/Bn\_FNews\_H.Feature.