Socially-Informed Content Analysis of Online Human Behavior

2025-09-13

AI Summary

Social media exacerbates political polarization, misinformation diffusion, and hate speech, fostering echo chambers and intergroup hostility. To address this, we propose a scalable representation learning framework that jointly models content semantics and social network topology to construct unified user embeddings, thereby uncovering sociocognitive drivers of online negativity and mechanisms of group fragmentation. Our method integrates network representation learning, user embedding modeling, fine-grained content analysis, and multilayer social network analysis. Key findings include: (1) moral diversity facilitates cross-ideological information diffusion; (2) social identity is a primary driver of toxic behavior; and (3) pandemic-related discourse exhibits asymmetric echo chamber effects and moral homophily. The framework enables high-accuracy prediction of user attributes, community affiliation, and behavioral tendencies, offering data-driven theoretical insights and actionable intervention strategies for cultivating healthy digital public spheres.

Technology Category

• Application Domains: Humanities & Computational Social Science
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community

Application Category

• Social Networks and Social Media: Media and governance, opinion dynamics, filter bubbles, polarization
• Web Mining and Content Analysis: Content-based information diffusion
• User Modeling, Personalization and Recommendation: Social recommender systems and personalization for the social Web

Abstract

The explosive growth of social media has not only revolutionized communication but also brought challenges such as political polarization, misinformation, hate speech, and echo chambers. This dissertation employs computational social science techniques to investigate these issues, understand the social dynamics driving negative online behaviors, and propose data-driven solutions for healthier digital interactions. I begin by introducing a scalable social network representation learning method that integrates user-generated content with social connections to create unified user embeddings, enabling accurate prediction and visualization of user attributes, communities, and behavioral propensities. Using this tool, I explore three interrelated problems: 1) COVID-19 discourse on Twitter, revealing polarization and asymmetric political echo chambers; 2) online hate speech, suggesting the pursuit of social approval motivates toxic behavior; and 3) moral underpinnings of COVID-19 discussions, uncovering patterns of moral homophily and echo chambers, while also indicating moral diversity and plurality can improve message reach and acceptance across ideological divides. These findings contribute to the advancement of computational social science and provide a foundation for understanding human behavior through the lens of social interactions and network homophily.

Problem

• Analyzing political polarization and echo chambers in social media
• Investigating motivations and dynamics behind online hate speech
• Exploring moral homophily's impact on cross-ideological communication

Innovation

• Scalable social network representation learning method
• Integrates user content with social connections
• Creates unified embeddings for behavior prediction