# ETMA: Efficient Transformer Based Multilevel Attention framework for Multimodal Fake News Detection

arXiv: https://arxiv.org/abs/2206.07331 · HTML: https://mlsanity.com/paper/2206.07331/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jun 2022.

## Built on by (most cited first, 5 of 5)

- [MMCFND: Multimodal Multilingual Caption-aware Fake News Detection for Low-resource Indic Languages](https://mlsanity.com/paper/2410.10407/) (2024)
- [CoVLM: Leveraging Consensus from Vision-Language Models for Semi-supervised Multi-modal Fake News Detection](https://mlsanity.com/paper/2410.04426/) (2024)
- [A New Hybrid Intelligent Approach for Multimodal Detection of Suspected Disinformation on TikTok](https://mlsanity.com/paper/2502.06893/) (2025)
- [A Cross-Modal Rumor Detection Scheme via Contrastive Learning by Exploring Text and Image internal Correlations](https://mlsanity.com/paper/2508.11141/) (2025)
- [F2IND-IT! -- Multimodal Fuzzy Fake Indian News Detection using Images and Text](https://mlsanity.com/paper/2605.17115/) (2026)

## Abstract (arXiv)

In this new digital era, social media has created a severe impact on the lives of people. In recent times, fake news content on social media has become one of the major challenging problems for society. The dissemination of fabricated and false news articles includes multimodal data in the form of text and images. The previous methods have mainly focused on unimodal analysis. Moreover, for multimodal analysis, researchers fail to keep the unique characteristics corresponding to each modality. This paper aims to overcome these limitations by proposing an Efficient Transformer based Multilevel Attention (ETMA) framework for multimodal fake news detection, which comprises the following components: visual attention-based encoder, textual attention-based encoder, and joint attention-based learning. Each component utilizes the different forms of attention mechanism and uniquely deals with multimodal data to detect fraudulent content. The efficacy of the proposed network is validated by conducting several experiments on four real-world fake news datasets: Twitter, Jruvika Fake News Dataset, Pontes Fake News Dataset, and Risdal Fake News Dataset using multiple evaluation metrics. The results show that the proposed method outperforms the baseline methods on all four datasets. Further, the computation time of the model is also lower than the state-of-the-art methods.