# Attacking Fake News Detectors via Manipulating News Social Engagement

arXiv: https://arxiv.org/abs/2302.07363 · HTML: https://mlsanity.com/paper/2302.07363/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Feb 2023.

## Built on by (most cited first, 23 of 23)

- [Can LLM-Generated Misinformation Be Detected?](https://mlsanity.com/paper/2309.13788/) (2023)
- [Better to Ask in English: Cross-Lingual Evaluation of Large Language Models for Healthcare Queries](https://mlsanity.com/paper/2310.13132/) (2023)
- [Fake News in Sheep's Clothing: Robust Fake News Detection Against LLM-Empowered Style Attacks](https://mlsanity.com/paper/2310.10830/) (2023)
- [DELL: Generating Reactions and Explanations for LLM-Based Misinformation Detection](https://mlsanity.com/paper/2402.10426/) (2024)
- [Can Knowledge Editing Really Correct Hallucinations?](https://mlsanity.com/paper/2410.16251/) (2024)
- [Can Editing LLMs Inject Harm?](https://mlsanity.com/paper/2407.20224/) (2024)
- [Modelling Political Coalition Negotiations Using LLM-based Agents](https://mlsanity.com/paper/2402.11712/) (2024)
- [A General Black-box Adversarial Attack on Graph-based Fake News Detectors](https://mlsanity.com/paper/2404.15744/) (2024)
- [Robust Fake News Detection using Large Language Models under Adversarial Sentiment Attacks](https://mlsanity.com/paper/2601.15277/) (2026)
- [Towards Fair Graph Anomaly Detection: Problem, Benchmark Datasets, and Evaluation](https://mlsanity.com/paper/2402.15988/) (2024)
- [Adversarial Style Augmentation via Large Language Model for Robust Fake News Detection](https://mlsanity.com/paper/2406.11260/) (2024)
- [The Paradigm Shift: A Comprehensive Survey on Large Vision Language Models for Multimodal Fake News Detection](https://mlsanity.com/paper/2601.15316/) (2026)
- [Provable Robustness of (Graph) Neural Networks Against Data Poisoning and Backdoor Attacks](https://mlsanity.com/paper/2407.10867/) (2024)
- [Adversarial Attacks Against Automated Fact-Checking: A Survey](https://mlsanity.com/paper/2509.08463/) (2025)
- [Fast Inference of Removal-Based Node Influence](https://mlsanity.com/paper/2403.08333/) (2024)
- [Multi-agent Systems for Misinformation Lifecycle : Detection, Correction And Source Identification](https://mlsanity.com/paper/2505.17511/) (2025)
- [RoBCtrl: Attacking GNN-Based Social Bot Detectors via Reinforced Manipulation of Bots Control Interaction](https://mlsanity.com/paper/2510.16035/) (2025)
- [Adversarial Robustness of Graph Transformers](https://mlsanity.com/paper/2407.11764/) (2024)
- [Unveiling the Threat of Fraud Gangs to Graph Neural Networks: Multi-Target Graph Injection Attacks Against GNN-Based Fraud Detectors](https://mlsanity.com/paper/2412.18370/) (2024)
- [CRC-SGAD: Conformal Risk Control for Supervised Graph Anomaly Detection](https://mlsanity.com/paper/2504.02248/) (2025)
- [On the Stability of Graph Convolutional Neural Networks: A Probabilistic Perspective](https://mlsanity.com/paper/2506.01213/) (2025)
- [Target-Aware Interaction-Guided Reinforcement Learning for Black-Box Node Injection Attacks on Graph Neural Networks](https://mlsanity.com/paper/2607.04091/) (2026)
- [Large Language Models in Misinformation Ecosystems: Misuse, Defense, and Vulnerability](https://mlsanity.com/paper/2607.10402/) (2026)

## Abstract (arXiv)

Social media is one of the main sources for news consumption, especially among the younger generation. With the increasing popularity of news consumption on various social media platforms, there has been a surge of misinformation which includes false information or unfounded claims. As various text- and social context-based fake news detectors are proposed to detect misinformation on social media, recent works start to focus on the vulnerabilities of fake news detectors. In this paper, we present the first adversarial attack framework against Graph Neural Network (GNN)-based fake news detectors to probe their robustness. Specifically, we leverage a multi-agent reinforcement learning (MARL) framework to simulate the adversarial behavior of fraudsters on social media. Research has shown that in real-world settings, fraudsters coordinate with each other to share different news in order to evade the detection of fake news detectors. Therefore, we modeled our MARL framework as a Markov Game with bot, cyborg, and crowd worker agents, which have their own distinctive cost, budget, and influence. We then use deep Q-learning to search for the optimal policy that maximizes the rewards. Extensive experimental results on two real-world fake news propagation datasets demonstrate that our proposed framework can effectively sabotage the GNN-based fake news detector performance. We hope this paper can provide insights for future research on fake news detection.