Not My Fault: Studying the Necessity of the User Classification & Employment of Fine-Level User-Based Moderation Interventions in Social Networks

Abstract

Various users with diverse spiritual moods and morals react differently to online content. On some users, highly toxic submissions cannot have a noticeable impact, while on another group, even not severely toxic content may provoke them to stop their interactive participation in social media. The same is true toward the intervention of platform holders, in the sense that one punishing moderation may lead a user to respect the community rules to a certain point, while the same action may stimulate the violent and offensive reaction of another user. In that regard, the moderation interventions of the platforms should follow a fine-level nature and be based on user behavior. It should also try to protect the communities the user cares about the most as far as the user, in turn, respects the content policies. The aim of the current study is to classify users into various behavioral groups, which can potentially provide the chance to adopt more efficient moderative measures to protect the community and give the user the feeling that he really deserves the moderative intervention that has experienced. Thus, the behavior of the core users of an already-banned controversial subreddit was taken into consideration, and a machine learning-based classification strategy was imposed on their activity level and their submission toxicity scores. Results have revealed interesting behavioral differences between users of the Reddit social media toward the taken moderations and have indicated the necessity for adopting find-level measures for protecting the platform, as well as the different behavioral groups.