The spread of radicalization and extremism through the Internet is a growing problem. We are witnessing a rise in online hate groups, inspiring the impressionable and vulnerable population towards extreme actions in the real world. Though the body of research to address these issues is growing in kind, they lack a key understanding of the structure and behavior of online extremist com- munities. In this thesis, we study the structure and behavior of extremist online communities and the spread of hateful sentiments through them to address this gap in the research. We propose a novel Graph-Based Approach to Studying the Spread of Radical Online Sentiment for studying the dynamics of online com- ment threads by representing them as graphs. Our Graph-Based Approach to Studying the Spread of Radical Online Sentiment allows us to leverage network analysis tools to reveal the most influential members in a social network and investigate sentiment propagation through the network as well. By combining sentiment analysis, social network analysis, and graph theory, we aim to shed light on the propagation of hate speech in online forums and the extent to which such speech can influence individuals. In this thesis, we pose four main research questions; firstly, to what ex- tent do connected members in an online comment thread and connected threads themselves share sentiment? Further, what is the impact of the frequency of interaction, measured by the degree of connection, on the sharing of sentiment?. Secondly, who are the most influential members in a comment thread, and how do they shape the sentiment in that thread? Thirdly, what does the sentiment of the thread look like over time as more members join threads and more comments are made? Finally, can the behavior of online sentiment spread be generalized? Can we develop a model for it? To answer these questions, we apply our Graph- Based Approach to Studying the Spread of Radical Online Sentiment to 1,973 long comment threads (30+ comments), totaling to 137k comments posted on dark-web forums. These threads contain a combination of benign posts and extremist comments on the Islamic religion from an unmoderated source. To answer our first research question, we constructed intra- and inter-thread graphs where we could analyze weighted and unweighted connections between threads and members within threads. Our results show that 73% of connected members within a comment thread shares a similar sentiment, and 64% of connected com- ment threads share a similar sentiment on the inter-thread level when weighted by the degree of connection. Additionally, we found the most influential mem- bers of our graphs using information centrality. We found that the original poster was the most influential member in our comment threads 57% of the time, with the mean sentiment of the thread matching the sentiment of the original poster. For our third research question, we performed a temporal anal- ysis of our threads. This analysis further supported our findings in our second research question. Over time, the majority of our threads had their overall sen- timent regress to the sentiment of the original poster, with the original poster being the member with the highest influence for 40% of the time steps. For our fourth and final research question, we used our understanding of our comment threads to create a model that can classify the sentiment of a thread member based on the members they are connected with. We achieved 87% accuracy with our classification model and further used it as a sentiment contagion model, which predicted the sentiment of a new member to a thread based on existing members with 72% accuracy. We plan to expand our study and further the robustness of our models on larger data sets and incorporate stance detection tools.

Library of Congress Subject Headings

Online hate speech; Radicalization; Social sciences--Network analysis; Graph theory; Contagion (Social psychology)

Publication Date


Document Type


Student Type


Degree Name

Data Science (MS)

Department, Program, or Center

Software Engineering (GCCIS)


Nidhi Rastogi

Advisor/Committee Member

Ashique KhudaBukhsh

Advisor/Committee Member

Andy Meneely


RIT – Main Campus

Plan Codes