# Multimodal Fusion with BERT and Attention Mechanism for Fake News Detection

arXiv: https://arxiv.org/abs/2104.11476 · HTML: https://mlsanity.com/paper/2104.11476/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 28 references that are in the map.
- First posted on arXiv 23 Apr 2021.

## Builds on (most cited first, 9 of 9)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [BERTweet: A pre-trained language model for English Tweets](https://mlsanity.com/paper/2005.10200/) (2020)
- [Fake News Detection on Social Media: A Data Mining Perspective](https://mlsanity.com/paper/1708.01967/) (2017)
- [Network-based Fake News Detection: A Pattern-driven Approach](https://mlsanity.com/paper/1906.04210/) (2019)
- [TI-CNN: Convolutional Neural Networks for Fake News Detection](https://mlsanity.com/paper/1806.00749/) (2018)
- [TIB's Visual Analytics Group at MediaEval '20: Detecting Fake News on Corona Virus and 5G Conspiracy](https://mlsanity.com/paper/2101.03529/) (2021)

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

- [Multi-modal Machine Learning in Engineering Design: A Review and Future Directions](https://mlsanity.com/paper/2302.10909/) (2023)
- [Multi-modal Machine Learning for Vehicle Rating Predictions Using Image, Text, and Parametric Data](https://mlsanity.com/paper/2305.15218/) (2023)
- [Similarity-Aware Multimodal Prompt Learning for Fake News Detection](https://mlsanity.com/paper/2304.04187/) (2023)
- [Head-wise Modality Specialization within MLLMs for Robust Fake News Detection under Missing Modality](https://mlsanity.com/paper/2604.09711/) (2026)
- [WISE: Web Information Satire and Fakeness Evaluation](https://mlsanity.com/paper/2512.24000/) (2025)

## Abstract (arXiv)

Fake news detection is an important task for increasing the credibility of information on the media since fake news is constantly spreading on social media every day and it is a very serious concern in our society. Fake news is usually created by manipulating images, texts, and videos. In this paper, we present a novel method for detecting fake news by fusing multimodal features derived from textual and visual data. Specifically, we used a pre-trained BERT model to learn text features and a VGG-19 model pre-trained on the ImageNet dataset to extract image features. We proposed a scale-dot product attention mechanism to capture the relationship between text features and visual features. Experimental results showed that our approach performs better than the current state-of-the-art method on a public Twitter dataset by 3.1% accuracy.