Characterizing, Detecting, and Predicting Online Ban Evasion

TL;DR

This paper presents a data-driven approach to identify and predict online ban evasion by analyzing behavioral similarities between banned users and their evading accounts, improving moderation efficiency.

Contribution

It introduces the first dataset and methods for detecting ban evasion, revealing behavioral patterns and training classifiers with high accuracy for early detection.

Findings

Behavioral similarities between parent and child accounts in usernames, pages, and content.

High accuracy in predicting future evaders (AUC = 0.78) and early detection (AUC = 0.85).

Effective matching of evading accounts to their banned counterparts (MRR = 0.97).

Abstract

Moderators and automated methods enforce bans on malicious users who engage in disruptive behavior. However, malicious users can easily create a new account to evade such bans. Previous research has focused on other forms of online deception, like the simultaneous operation of multiple accounts by the same entities (sockpuppetry), impersonation of other individuals, and studying the effects of de-platforming individuals and communities. Here we conduct the first data-driven study of ban evasion, i.e., the act of circumventing bans on an online platform, leading to temporally disjoint operation of accounts by the same user. We curate a novel dataset of 8,551 ban evasion pairs (parent, child) identified on Wikipedia and contrast their behavior with benign users and non-evading malicious users. We find that evasion child accounts demonstrate similarities with respect to their banned parent accounts on several behavioral axes - from similarity in usernames and edited pages to similarity in content added to the platform and its psycholinguistic attributes. We reveal key behavioral attributes of accounts that are likely to evade bans. Based on the insights from the analyses, we train logistic regression classifiers to detect and predict ban evasion at three different points in the ban evasion lifecycle. Results demonstrate the effectiveness of our methods in predicting future evaders (AUC = 0.78), early detection of ban evasion (AUC = 0.85), and matching child accounts with parent accounts (MRR = 0.97). Our work can aid moderators by reducing their workload and identifying evasion pairs faster and more efficiently than current manual and heuristic-based approaches. Dataset is available https://github.com/srijankr/ban_evasion.