# GAME-ON: Graph Attention Network based Multimodal Fusion for Fake News Detection

arXiv: https://arxiv.org/abs/2202.12478 · HTML: https://mlsanity.com/paper/2202.12478/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 79 references that are in the map.
- First posted on arXiv 25 Feb 2022.

## Builds on (most cited first, 30 of 30)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [AdapterHub: A Framework for Adapting Transformers](https://mlsanity.com/paper/2007.07779/) (2020)
- [Fake News Detection on Social Media: A Data Mining Perspective](https://mlsanity.com/paper/1708.01967/) (2017)
- [Rumor Detection on Social Media with Bi-Directional Graph Convolutional Networks](https://mlsanity.com/paper/2001.06362/) (2020)
- [Fake News Detection on Social Media using Geometric Deep Learning](https://mlsanity.com/paper/1902.06673/) (2019)
- [A Stylometric Inquiry into Hyperpartisan and Fake News](https://mlsanity.com/paper/1702.05638/) (2017)
- [MDFEND: Multi-domain Fake News Detection](https://mlsanity.com/paper/2201.00987/) (2022)
- [A Novel Graph-based Multi-modal Fusion Encoder for Neural Machine Translation](https://mlsanity.com/paper/2007.08742/) (2020)
- [Exploiting Multi-domain Visual Information for Fake News Detection](https://mlsanity.com/paper/1908.04472/) (2019)
- [Improving Fake News Detection by Using an Entity-enhanced Framework to Fuse Diverse Multimodal Clues](https://mlsanity.com/paper/2108.10509/) (2021)
- [Multi-Modal Graph Neural Network for Joint Reasoning on Vision and Scene Text](https://mlsanity.com/paper/2003.13962/) (2020)
- [Graph Neural Networks with Continual Learning for Fake News Detection from Social Media](https://mlsanity.com/paper/2007.03316/) (2020)
- [CM-GANs: Cross-modal Generative Adversarial Networks for Common Representation Learning](https://mlsanity.com/paper/1710.05106/) (2017)
- [Integrating Pattern- and Fact-based Fake News Detection via Model Preference Learning](https://mlsanity.com/paper/2109.11333/) (2021)
- [KBGN: Knowledge-Bridge Graph Network for Adaptive Vision-Text Reasoning in Visual Dialogue](https://mlsanity.com/paper/2008.04858/) (2020)
- [Identifying Possible Rumor Spreaders on Twitter: A Weak Supervised Learning Approach](https://mlsanity.com/paper/2010.07647/) (2020)
- [DEAP-FAKED: Knowledge Graph based Approach for Fake News Detection](https://mlsanity.com/paper/2107.10648/) (2021)
- [Misinformation Detection on YouTube Using Video Captions](https://mlsanity.com/paper/2107.00941/) (2021)
- [Meet The Truth: Leverage Objective Facts and Subjective Views for Interpretable Rumor Detection](https://mlsanity.com/paper/2107.10747/) (2021)
- [Analyzing Unaligned Multimodal Sequence via Graph Convolution and Graph Pooling Fusion](https://mlsanity.com/paper/2011.13572/) (2020)
- [What goes on inside rumour and non-rumour tweets and their reactions: A Psycholinguistic Analyses](https://mlsanity.com/paper/2112.03003/) (2021)
- [HyperLearn: A Distributed Approach for Representation Learning in Datasets With Many Modalities](https://mlsanity.com/paper/1909.09252/) (2019)
- [Misleading the Covid-19 vaccination discourse on Twitter: An exploratory study of infodemic around the pandemic](https://mlsanity.com/paper/2108.10735/) (2021)
- [MEG: Multi-Evidence GNN for Multimodal Semantic Forensics](https://mlsanity.com/paper/2011.11286/) (2020)
- [News Labeling as Early as Possible: Real or Fake?](https://mlsanity.com/paper/1906.03423/) (2019)

## Built on by (most cited first, 7 of 7)

- [Multi-modal Misinformation Detection: Approaches, Challenges and Opportunities](https://mlsanity.com/paper/2203.13883/) (2022)
- [Towards Unified Multimodal Misinformation Detection in Social Media: A Benchmark Dataset and Baseline](https://mlsanity.com/paper/2509.25991/) (2025)
- [Reinforcement Learning-based Knowledge Graph Reasoning for Explainable Fact-checking](https://mlsanity.com/paper/2310.07613/) (2023)
- [Misinformation Concierge: A Proof-of-Concept with Curated Twitter Dataset on COVID-19 Vaccination](https://mlsanity.com/paper/2309.00639/) (2023)
- [Crowd Intelligence for Early Misinformation Prediction on Social Media](https://mlsanity.com/paper/2408.04463/) (2024)
- [AMIR: Automated MisInformation Rebuttal -- A COVID-19 Vaccination Datasets based Recommendation System](https://mlsanity.com/paper/2310.19834/) (2023)
- [M4-BLIP: Advancing Multi-Modal Media Manipulation Detection through Face-Enhanced Local Analysis](https://mlsanity.com/paper/2512.01214/) (2025)

## Abstract (arXiv)

Social media in present times has a significant and growing influence. Fake news being spread on these platforms have a disruptive and damaging impact on our lives. Furthermore, as multimedia content improves the visibility of posts more than text data, it has been observed that often multimedia is being used for creating fake content. A plethora of previous multimodal-based work has tried to address the problem of modeling heterogeneous modalities in identifying fake content. However, these works have the following limitations: (1) inefficient encoding of inter-modal relations by utilizing a simple concatenation operator on the modalities at a later stage in a model, which might result in information loss; (2) training very deep neural networks with a disproportionate number of parameters on small but complex real-life multimodal datasets result in higher chances of overfitting. To address these limitations, we propose GAME-ON, a Graph Neural Network based end-to-end trainable framework that allows granular interactions within and across different modalities to learn more robust data representations for multimodal fake news detection. We use two publicly available fake news datasets, Twitter and Weibo, for evaluations. Our model outperforms on Twitter by an average of 11% and keeps competitive performance on Weibo, within a 2.6% margin, while using 65% fewer parameters than the best comparable state-of-the-art baseline.