Date of Award

8-17-2020

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Computer Science

First Advisor

Nitin Agarwal

Abstract

The increased global salience of social media has fueled the propagation of various forms of toxicity. As digital technologies and interactive websites proliferate, Online Social Networks (OSNs), once regarded as safe havens for sharing information and providing mutual support among groups of people, have now become breeding grounds for spreading toxic behaviors, political propaganda and radicalizing content. Numerous platforms are trying to combat this phenomenon by training computational methods that are capable of automatically recognizing these toxic contents and removing them from the user-generated text on their platforms. However, given the immensity and speed of content posted on online platforms, identifying and deterring these behaviors at scale remains challenging. To facilitate research in this direction, in this dissertation, I present four studies towards assessing the role of social media platforms in the propagation of toxicity. Specifically, this dissertation aims to answer the following research questions: RQ1: What is the relationship between discussion themes and the pervasiveness of online toxicity? RQ2: Can latent features of initial toxicity be identified and used to predict further toxicity? RQ3: Is toxicity contagious? RQ4: Are toxic users/themes clustered/segregated on social media? To answer these questions, in study 1, I outlined a methodology for identifying and scoring toxicity within the user-generated content posted on YouTube. This analysis was able to shed light on how the existence and magnitude of toxicity changes when there are shifts in the narrative. In study 2, I leveraged the Non-negative Matrix Factorization (NMF) technique to describe the relationships between the impacts of toxicity of a video on its comments. This novel application of the NMF technique allowed toxicity predictions by utilizing the latent features generated based on a prior toxicity matrix. To determine if toxicity is contagious, in study 3, I proposed a technique grounded on mathematical epidemiology to understand and explain the spread of toxicity on YouTube. In study 4, I applied the Social Network Analysis (SNA) technique to demonstrate the existence of clusters of toxic users/themes on YouTube. Finally, I outlined some strategies for mitigating and responding to online toxicity and presented a few ideas for future work.

Download

Share

COinS