Abstract:
With the rise of online social networks, identifying or classifying real-life relationships between users has become useful for financial fraud detection. People in different relationships usually give each other meaningful gifts on different dates.
The red packet is a traditional Asian monetary gift, especially in China. With the rapid growth of the Internet, people started giving electronic red packets instead of paper ones for money gifts on social media. This paper proposes a novel method for relationship identification on WeChat, one of China’s largest social platforms, using red packet interactions.
We analyze the WeChat red packets network to identify real-life relationship types between users by mining semantic information about red packet amounts and sending times. On one hand, we construct an Amount-Date Graph and apply the graph embedding method to learn embeddings of the amount and sending date of each red packet to better capture user red packet gifting behaviors for relationship identification.
However, we propose a novel sequential model, Cross & Attention Sequence Model (CASM), which explicitly learns the interactions between the latent semantic information of each red packet’s amount and sending date in the sequence between two users. We test our method on a real-world WeChat Users Red Packets dataset with 8 types of relationships. Our approach outperforms baselines and achieves 81.70 percent prediction accuracy in experiments.
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