  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.
