Evaluating Moderation in Online Social Network

TL;DR

This paper introduces a simulation framework extending the SEIZ epidemic model to evaluate the effectiveness of generic and profile-aware moderation strategies in controlling toxic content spread on social networks.

Contribution

It presents a novel simulation-based approach incorporating user psychological profiles for personalized moderation, advancing the understanding of intervention impacts.

Findings

Profile-aware moderation significantly reduces toxicity spread.

Generic interventions are less effective than personalized strategies.

The framework allows systematic exploration of moderation parameters.

Abstract

The spread of toxic content on online platforms presents complex challenges that call for both theoretical insight and practical tools to test intervention strategies. In this novel research paper, we introduce a simulation-based framework that extends the classical SEIZ (Susceptible-Exposed-Infected-Skeptic) epidemic model to capture the dynamics of toxic message propagation. Our simulator incorporates active moderation mechanisms through two distinct variants: a basic moderator, which implements uniform, non-personalized interventions, and smart moderator, which leverages user-specific psychological profiles based on Dark Triad traits to apply personalized, threshold-driven moderation. By varying parameter configurations, the simulator allows for systematic exploration of how different moderation strategies influence user state transitions over time. Simulation results demonstrate that while generic interventions can curb toxicity under certain conditions, profile-aware moderation proves significantly more effective in limiting both the spread and persistence of toxic behavior. This simulation framework offers a flexible and extensible tool for studying and designing adaptive moderation strategies in complex online social systems.