Hybrid Feature Combinations with CNN for Bangla Fake News Classification

2026-05-17

AI Summary

This study addresses the challenge of rampant fake news in Bengali on social media and the limited efficacy of existing detection approaches. To tackle this issue, the authors propose a convolutional neural network (CNN) model that integrates semantic, statistical, and character-level features. Through systematic evaluation, the work demonstrates for the first time the synergistic benefits of combining these heterogeneous features, revealing their significant contribution to enhancing model performance. Experimental results on the BanFakeNews-2.0 dataset show that the proposed method substantially improves recall and F1-score compared to single-feature baselines, offering an effective solution for fake news detection in low-resource languages such as Bengali.

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.

Problem

• Fake News Detection
• Bangla Language
• Social Media
• Misinformation
• News Credibility

Innovation

• hybrid features
• CNN
• Bangla fake news detection
• feature combination
• BanFakeNews-2.0