Preemptive Detection of Fake Accounts on Social Networks via Multi-Class Preferential Attachment Classifiers

TL;DR

This paper introduces PreAttacK, a novel multi-class preferential attachment classifier that detects fake social network accounts early, before they establish friendships, with provable guarantees and state-of-the-art accuracy on Facebook.

Contribution

The paper develops a new algorithm based on a multi-class preferential attachment model, providing the first provable guarantees for early fake account detection without relying on content or friendship data.

Findings

PreAttacK achieves AUC=0.9 after 20 friend requests.

It outperforms existing methods that need 100 friend requests.

Provides the first provable guarantees for early fake account detection.

Abstract

In this paper, we describe a new algorithm called Preferential Attachment k-class Classifier (PreAttacK) for detecting fake accounts in a social network. Recently, several algorithms have obtained high accuracy on this problem. However, they have done so by relying on information about fake accounts' friendships or the content they share with others--the very things we seek to prevent. PreAttacK represents a significant departure from these approaches. We provide some of the first detailed distributional analyses of how new fake (and real) accounts first attempt to request friends after joining a major network (Facebook). We show that even before a new account has made friends or shared content, these initial friend request behaviors evoke a natural multi-class extension of the canonical Preferential Attachment model of social network growth. We use this model to derive a new algorithm, PreAttacK. We prove that in relevant problem instances, PreAttacK near-optimally approximates the posterior probability that a new account is fake under this multi-class Preferential Attachment model of new accounts' (not-yet-answered) friend requests. These are the first provable guarantees for fake account detection that apply to new users, and that do not require strong homophily assumptions. This principled approach also makes PreAttacK the only algorithm with provable guarantees that obtains state-of-the-art performance on new users on the global Facebook network, where it converges to AUC=0.9 after new users send + receive a total of just 20 not-yet-answered friend requests. For comparison, state-of-the-art benchmarks do not obtain this AUC even after observing additional data on new users' first 100 friend requests. Thus, unlike mainstream algorithms, PreAttacK converges before the median new fake account has made a single friendship (accepted friend request) with a human.