# Automatic Detection of Satire in Bangla Documents: A CNN Approach Based on Hybrid Feature Extraction Model

arXiv: https://arxiv.org/abs/1911.11062 · HTML: https://mlsanity.com/paper/1911.11062/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Nov 2019.

## Built on by (most cited first, 2 of 2)

- [Tackling Fake News in Bengali: Unraveling the Impact of Summarization vs. Augmentation on Pre-trained Language Models](https://mlsanity.com/paper/2307.06979/) (2023)
- [Bangla Fake News Detection Based On Multichannel Combined CNN-LSTM](https://mlsanity.com/paper/2503.04781/) (2025)

## Abstract (arXiv)

Widespread of satirical news in online communities is an ongoing trend. The nature of satires is so inherently ambiguous that sometimes it's too hard even for humans to understand whether it's actually satire or not. So, research interest has grown in this field. The purpose of this research is to detect Bangla satirical news spread in online news portals as well as social media. In this paper, we propose a hybrid technique for extracting features from text documents combining Word2Vec and TF-IDF. Using our proposed feature extraction technique, with standard CNN architecture we could detect whether a Bangla text document is satire or not with an accuracy of more than 96%.