Saudi Cultural Missions Theses & Dissertations
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Item Restricted Quantifying and Profiling Echo Chambers on Social Media(Arizona State University, 2024) Alatawi, Faisal; Liu, Huan; Sen, Arunabha; Davulcu, Hasan; Shu, KaiEcho chambers on social media have become a critical focus in the study of online behavior and public discourse. These environments, characterized by the ideological homogeneity of users and limited exposure to opposing viewpoints, contribute to polarization, the spread of misinformation, and the entrenchment of biases. While significant research has been devoted to proving the existence of echo chambers, less attention has been given to understanding their internal dynamics. This dissertation addresses this gap by developing novel methodologies for quantifying and profiling echo chambers, with the goal of providing deeper insights into how these communities function and how they can be measured. The first core contribution of this work is the introduction of the Echo Chamber Score (ECS), a new metric for measuring the degree of ideological segregation in social media interaction networks. The ECS captures both the cohesion within communities and the separation between them, offering a more nuanced approach to assessing polarization. By using a self-supervised Graph Auto-Encoder (EchoGAE), the ECS bypasses the need for explicit ideological labeling, instead embedding users based on their interactions and linguistic patterns. The second contribution is a Heterogeneous Information Network (HIN)-based framework for profiling echo chambers. This framework integrates social and linguistic features, allowing for a comprehensive analysis of the relationships between users, topics, and language within echo chambers. By combining community detection, topic modeling, and language analysis, the profiling method reveals how discourse and group behavior reinforce ideological boundaries. Through the application of these methods to real-world social media datasets, this dissertation demonstrates their effectiveness in identifying polarized communities and profiling their internal discourse. The findings highlight how linguistic homophily and social identity theory shape echo chambers and contribute to polarization. Overall, this research advances the understanding of echo chambers by moving beyond detection to explore their structural and linguistic complexities, offering new tools for measuring and addressing polarization on social media platforms.24 0Item Restricted Exploring Emoji Sentiment Roles in Arabic Textual Content on Digital Social Networks(Saudi Digital Library, 2024-07-09) Hakami, Shatha Ali A; Hendley, Robert; Smith, PhillipIn today’s digital landscape, emoji have risen as pivotal elements in articulating sentiment, especially within the intricacies of the Arabic language. This thesis examines the various roles that emoji can play in expressing sentiment in Arabic texts, highlighting their relevance both in academic and real-world contexts. Beginning with foundational insights, our investigation retraces the history of emoji as important non-verbal communicative tools in human interaction. Then, we explore the distinct challenges of sentiment analysis in Arabic and refer to a thorough review of previous studies to frame our method, identifying both established techniques and unexplored opportunities. At the heart of our research is the understanding that, depending on the context, an emoji can adopt a wide variety of sentiment roles. These range from acting as an indicator, mitigator, emphasizer, reverser, releaser, or trigger of either negative or positive sentiment. Additionally, there are instances where an emoji simply maintains a neutral effect on the sentiment of the accompanying text. To achieve this, we gathered a large dataset, mainly from Twitter, and developed lexicons of words and emoji tailored for sentiment analysis in Arabic. These lexicons were the basis of our analysis model. By leveraging the insights gained from the emoji-roles sentiment lexicon and combining them with our established knowledge of the sentiment roles associated with specific emoji patterns, we make a significant improvement in the conventional sentiment classifier based on the emoji lexicon. Traditional methods often assign a static sentiment score to an emoji, failing to consider its varying roles in different textual contexts. Our refined approach corrects this oversight. Instead of considering a singular unchanging sentiment score for each emoji, the classifier dynamically retrieves sentiment scores based on the specific role the emoji plays within a given sentence. In conclusion, we compare our method with other Arabic sentiment analysis tools, demonstrating the value of our approach, especially within nuanced linguistic phenomena such as sarcasm and humour. This thesis sets the foundation for future Arabic research in this expanding domain.60 0