# SCARLET: Explainable Attention based Graph Neural Network for Fake News spreader prediction

arXiv: https://arxiv.org/abs/2102.04627 · HTML: https://mlsanity.com/paper/2102.04627/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Feb 2021.

## Built on by (most cited first, 8 of 8)

- [Trustworthy Graph Neural Networks: Aspects, Methods and Trends](https://mlsanity.com/paper/2205.07424/) (2022)
- [A Survey on Explainability of Graph Neural Networks](https://mlsanity.com/paper/2306.01958/) (2023)
- [An Interactive Framework for Profiling News Media Sources](https://mlsanity.com/paper/2309.07384/) (2023)
- [Rhythm of Opinion: A Hawkes-Graph Framework for Dynamic Propagation Analysis](https://mlsanity.com/paper/2504.15072/) (2025)
- [Semantic Interpretation and Validation of Graph Attention-based Explanations for GNN Models](https://mlsanity.com/paper/2308.04220/) (2023)
- [SOMPS-Net : Attention based social graph framework for early detection of fake health news](https://mlsanity.com/paper/2111.11272/) (2021)
- [Enhancing Social Media Rumor Detection: A Semantic and Graph Neural Network Approach for the 2024 Global Election](https://mlsanity.com/paper/2503.01394/) (2025)
- [Predicting, Evaluating, and Explaining Top Misinformation Spreaders via Archetypal User Behavior](https://mlsanity.com/paper/2608.16323/) (2026)

## Abstract (arXiv)

False information and true information fact checking it, often co-exist in social networks, each competing to influence people in their spread paths. An efficient strategy here to contain false information is to proactively identify if nodes in the spread path are likely to endorse false information (i.e. further spread it) or refutation information (thereby help contain false information spreading). In this paper, we propose SCARLET (truSt and Credibility bAsed gRaph neuraL nEtwork model using aTtention) to predict likely action of nodes in the spread path. We aggregate trust and credibility features from a node's neighborhood using historical behavioral data and network structure and explain how features of a spreader's neighborhood vary. Using real world Twitter datasets, we show that the model is able to predict false information spreaders with an accuracy of over 87%.