# CACL: Community-Aware Heterogeneous Graph Contrastive Learning for Social Media Bot Detection

arXiv: https://arxiv.org/abs/2405.10558 · HTML: https://mlsanity.com/paper/2405.10558/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 May 2024.

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

- [TRACE-Bot: Detecting Emerging LLM-Driven Social Bots via Implicit Semantic Representations and AIGC-Enhanced Behavioral Patterns](https://mlsanity.com/paper/2604.02147/) (2026)
- [Graph-based Fake Account Detection: A Survey](https://mlsanity.com/paper/2507.06541/) (2025)
- [FedRio: Personalized Federated Social Bot Detection via Cooperative Reinforced Contrastive Adversarial Distillation](https://mlsanity.com/paper/2604.10678/) (2026)
- [Linking Heterogeneous Data with Coordinated Agent Flows for Social Media Analysis](https://mlsanity.com/paper/2510.26172/) (2025)
- [SAHG: Sector-Anisotropic Hyperbolic Graph Model for Social Bot Detection](https://mlsanity.com/paper/2605.30166/) (2026)

## Abstract (arXiv)

Social media bot detection is increasingly crucial with the rise of social media platforms. Existing methods predominantly construct social networks as graph and utilize graph neural networks (GNNs) for bot detection. However, most of these methods focus on how to improve the performance of GNNs while neglecting the community structure within social networks. Moreover, GNNs based methods still face problems such as poor model generalization due to the relatively small scale of the dataset and over-smoothness caused by information propagation mechanism. To address these problems, we propose a Community-Aware Heterogeneous Graph Contrastive Learning framework (CACL), which constructs social network as heterogeneous graph with multiple node types and edge types, and then utilizes community-aware module to dynamically mine both hard positive samples and hard negative samples for supervised graph contrastive learning with adaptive graph enhancement algorithms. Extensive experiments demonstrate that our framework addresses the previously mentioned challenges and outperforms competitive baselines on three social media bot benchmarks.