Modeling User Behavior With Interaction Networks for Spam Detection

TL;DR

This paper introduces SEINE, a novel graph-based model for detecting spammers on large-scale platforms by capturing complex user behaviors and interactions, achieving high accuracy and efficiency.

Contribution

The paper presents SEINE, a new interaction network model that effectively detects spammers on billion-scale graphs, outperforming existing methods in large-scale scenarios.

Findings

Achieves 80% recall at 1% false positive rate on real data

Performs comparably to state-of-the-art on public datasets

Capable of handling billion-scale graphs efficiently

Abstract

Spam is a serious problem plaguing web-scale digital platforms which facilitate user content creation and distribution. It compromises platform's integrity, performance of services like recommendation and search, and overall business. Spammers engage in a variety of abusive and evasive behavior which are distinct from non-spammers. Users' complex behavior can be well represented by a heterogeneous graph rich with node and edge attributes. Learning to identify spammers in such a graph for a web-scale platform is challenging because of its structural complexity and size. In this paper, we propose SEINE (Spam DEtection using Interaction NEtworks), a spam detection model over a novel graph framework. Our graph simultaneously captures rich users' details and behavior and enables learning on a billion-scale graph. Our model considers neighborhood along with edge types and attributes, allowing it to capture a wide range of spammers. SEINE, trained on a real dataset of tens of millions of nodes and billions of edges, achieves a high performance of 80% recall with 1% false positive rate. SEINE achieves comparable performance to the state-of-the-art techniques on a public dataset while being pragmatic to be used in a large-scale production system.