A child rights advocacy organisation in the United States is accusing Google of bypassing parental authority by allowing children to disable parental supervision over Google accounts after they turn 13. Melissa McKay, president of the Digital Childhood Institute, stated on LinkedIn that Google sent her 12-year-old an email that will unlock additional tools once he turns 13, posting screenshots of the email. Among the changes, once children turn the age of 13, they can turn off supervised experiences on YouTube and can add payment methods to Google Pay. Parents will no longer be able to block apps, turn on location sharing without the permission of the child user or block access to payment features. "Google is asserting authority over a boundary that does not belong to them. It reframes parents as a temporary inconvenience to be outgrown and positions corporate platforms as the default replacement," McKay said in a post on LinkedIn.

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.