A computational social science that makes sense Studying the complex dynamics of social interaction and collective meaning-making

How do ideas spread and evolve? In what ways do our social circles shape our thinking? What fuels online polarization? With access to unprecedented levels of digital data and computational tools, Computational Social Science (CSS) has emerged as a powerful interdisciplinary field for studying social dynamics at scale. A complexity-inspired branch of CSS models phenomena such as polarization, misinformation, and belief propagation through system dynamics. It has proven invaluable for understanding emergent and previously difficult-to-explain shifts in collective social outcomes. Yet the field often prioritizes structural patterns of social interaction over interpretation: ideas are modeled as spreading like viruses, while social interactions are reduced to quantifiable networks. This thesis builds on a growing body of research that studies the intersection of social interaction and interpretation, advancing the field by integrating insights from relational sociology with complexity-inspired CSS to examine how meaning-making shapes idea diffusion and polarization. Through four case studies, this thesis highlights the role of key individuals in translating concepts across contexts and demonstrates how conflict and disagreement fuel online polarization. This work underscores how collective meaning-making shapes social dynamics, offering insights that are both academically significant and crucial for addressing pressing societal challenges, including misinformation and polarization.