Hybrid Feature Combinations with CNN for Bangla Fake News Classification
For researchers working on low-resource language fake news detection, this is an incremental study applying known feature combination techniques to a Bangla dataset.
This work explores feature selection approaches for Bangla fake news detection, showing that combining semantic, statistical, and character-level features improves recall and F1-scores over individual features on the BanFakeNews-2.0 dataset using a CNN model.
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.