Date of Award

12-4-2024

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Computer Science

First Advisor

Nitin Agarwal

Abstract

This dissertation explores the spread and intensity of toxic content on social media, with a particular focus on platform X (formerly known as Twitter). The growing presence of toxic interactions has sparked concerns about their effects on public discourse. Utilizing epidemiological models such as SIR (Susceptible, Infected, Recovered), SIS (Susceptible, Infected, Susceptible), SIRS (Susceptible, Infected, Recovered, Susceptible), SEIRS (Susceptible, Exposed, Infected, Recovered, Susceptible), and the novel SEImIhRS model (Susceptible, Exposed, Moderate Infected, High Infected, Recovered, Susceptible), which are traditionally used to study infectious disease dynamics, this dissertation examines toxicity in platform X discussions. A key contribution of this work is its focus on the intensity of toxicity, which previous studies have not explored in depth. In the dissertation, users are categorized into moderate and highly infected groups to represent different levels of toxicity severity. To evaluate the effectiveness of these models, five diverse datasets encompassing various types of social media content are utilized. Among the models, the SEImIhRS model outperforms the others, showing lower error rates across all datasets. This suggests that it offers a more accurate depiction of how toxicity spreads in digital environments. The results of this research provide an advanced analytical framework for comprehending online toxicity dynamics. By identifying the most effective model for simulating toxicity propagation, the study aids policymakers and social media platforms in developing targeted interventions and improving content moderation systems. This approach aims to create healthier online spaces by effectively addressing and reducing toxic behavior on social media.

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