  The rise of the click farm business using Multi-purpose Messaging Mobile Apps
(MMMAs) tempts cybercriminals to perpetrate crowdsourcing frauds that cause
financial losses to click farm workers. In this paper, we propose a novel
contrastive multi-view learning method named CMT for crowdsourcing fraud
detection over the heterogeneous temporal graph (HTG) of MMMA. CMT captures
both heterogeneity and dynamics of HTG and generates high-quality
representations for crowdsourcing fraud detection in a self-supervised manner.
We deploy CMT to detect crowdsourcing frauds on an industry-size HTG of a
representative MMMA WeChat and it significantly outperforms other methods. CMT
also shows promising results for fraud detection on a large-scale public
financial HTG, indicating that it can be applied in other graph anomaly
detection tasks. We provide our implementation at
https://github.com/KDEGroup/CMT.
