# GCAN: Graph-aware Co-Attention Networks for Explainable Fake News Detection on Social Media

arXiv: https://arxiv.org/abs/2004.11648 · HTML: https://mlsanity.com/paper/2004.11648/

## Key facts

- Cited by 109 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Apr 2020.

## Built on by (most cited first, 30 of 109)

- [A Survey on Automated Fact-Checking](https://mlsanity.com/paper/2108.11896/) (2021)
- [A Comprehensive Survey on Graph Anomaly Detection with Deep Learning](https://mlsanity.com/paper/2106.07178/) (2021)
- [Fact-Checking Complex Claims with Program-Guided Reasoning](https://mlsanity.com/paper/2305.12744/) (2023)
- [Towards LLM-based Fact Verification on News Claims with a Hierarchical Step-by-Step Prompting Method](https://mlsanity.com/paper/2310.00305/) (2023)
- [Trustworthy Graph Neural Networks: Aspects, Methods and Trends](https://mlsanity.com/paper/2205.07424/) (2022)
- [User Preference-aware Fake News Detection](https://mlsanity.com/paper/2104.12259/) (2021)
- [Fake News in Sheep's Clothing: Robust Fake News Detection Against LLM-Empowered Style Attacks](https://mlsanity.com/paper/2310.10830/) (2023)
- [Generating Literal and Implied Subquestions to Fact-check Complex Claims](https://mlsanity.com/paper/2205.06938/) (2022)
- [A General Survey on Attention Mechanisms in Deep Learning](https://mlsanity.com/paper/2203.14263/) (2022)
- [Multimodal Automated Fact-Checking: A Survey](https://mlsanity.com/paper/2305.13507/) (2023)
- [Explainable Fake News Detection With Large Language Model via Defense Among Competing Wisdom](https://mlsanity.com/paper/2405.03371/) (2024)
- [Can LLMs Produce Faithful Explanations For Fact-checking? Towards Faithful Explainable Fact-Checking via Multi-Agent Debate](https://mlsanity.com/paper/2402.07401/) (2024)
- [TwiBot-22: Towards Graph-Based Twitter Bot Detection](https://mlsanity.com/paper/2206.04564/) (2022)
- [DELL: Generating Reactions and Explanations for LLM-Based Misinformation Detection](https://mlsanity.com/paper/2402.10426/) (2024)
- [Towards Propagation Uncertainty: Edge-enhanced Bayesian Graph Convolutional Networks for Rumor Detection](https://mlsanity.com/paper/2107.11934/) (2021)
- [Towards Fine-Grained Reasoning for Fake News Detection](https://mlsanity.com/paper/2110.15064/) (2021)
- [Rumor Detection on Twitter with Claim-Guided Hierarchical Graph Attention Networks](https://mlsanity.com/paper/2110.04522/) (2021)
- [A Survey on Explainability of Graph Neural Networks](https://mlsanity.com/paper/2306.01958/) (2023)
- [JustiLM: Few-shot Justification Generation for Explainable Fact-Checking of Real-world Claims](https://mlsanity.com/paper/2401.08026/) (2024)
- [The Surprising Performance of Simple Baselines for Misinformation Detection](https://mlsanity.com/paper/2104.06952/) (2021)
- [QACHECK: A Demonstration System for Question-Guided Multi-Hop Fact-Checking](https://mlsanity.com/paper/2310.07609/) (2023)
- [Benchmarking the Generation of Fact Checking Explanations](https://mlsanity.com/paper/2308.15202/) (2023)
- [What Does the Bot Say? Opportunities and Risks of Large Language Models in Social Media Bot Detection](https://mlsanity.com/paper/2402.00371/) (2024)
- [Multimodal Emergent Fake News Detection via Meta Neural Process Networks](https://mlsanity.com/paper/2106.13711/) (2021)
- [Abstract, Rationale, Stance: A Joint Model for Scientific Claim Verification](https://mlsanity.com/paper/2110.15116/) (2021)
- [Explainable Identification of Dementia from Transcripts using Transformer Networks](https://mlsanity.com/paper/2109.06980/) (2021)
- [Generating Fluent Fact Checking Explanations with Unsupervised Post-Editing](https://mlsanity.com/paper/2112.06924/) (2021)
- [Multi-modal Misinformation Detection: Approaches, Challenges and Opportunities](https://mlsanity.com/paper/2203.13883/) (2022)
- [Graph Learning for Anomaly Analytics: Algorithms, Applications, and Challenges](https://mlsanity.com/paper/2212.05532/) (2022)
- [It's about Time: Rethinking Evaluation on Rumor Detection Benchmarks using Chronological Splits](https://mlsanity.com/paper/2302.03147/) (2023)

## Abstract (arXiv)

This paper solves the fake news detection problem under a more realistic scenario on social media. Given the source short-text tweet and the corresponding sequence of retweet users without text comments, we aim at predicting whether the source tweet is fake or not, and generating explanation by highlighting the evidences on suspicious retweeters and the words they concern. We develop a novel neural network-based model, Graph-aware Co-Attention Networks (GCAN), to achieve the goal. Extensive experiments conducted on real tweet datasets exhibit that GCAN can significantly outperform state-of-the-art methods by 16% in accuracy on average. In addition, the case studies also show that GCAN can produce reasonable explanations.