Community Shaping in the Digital Age: A Risk‐Focused Temporal Fusion Framework for Analyzing Information Diffusion and Fragmentation in Online Social Networks

Understanding the dynamics of online communities is crucial for comprehending modern social interactions and information dissemination. This research aims to understand how communities unfold and behave over time in the online environment of social media platforms by presenting a framework based on temporal fusion of information about text and network-type data. By employing text classification within identified communities, we uncover the underlying mechanisms that drive community formation and evolution in the online space. A dynamic social network analysis further reveals how real-world circumstances influence the development and interactions within these communities. Finally, we have identified fourteen key elements based on social science theories that encapsulate the insights expected from social structure and dynamics, and we have used the introduced methodology to evaluate how each key element enhances our understanding of social media dynamics, resulting in presenting our framework as a suited methodology for discourse fragmentation analysis. The framework is validated through a case study analyzing X (Twitter) data during major national circumstances in the United States in 2020. The discrimination discourse was found at the center of our analyses, and sexism, racism, xenophobia, ableism, homophobia, and religious intolerance are the fragments of the main discourse. Results show that the cycle of emergence and dissolution of the communities is fast and very representative of the discourse fragments. Real-world circumstances can impact the discourse fragments and their dominance, and comparing the number of distinct communities and their overlap, we reveal how social media can contribute to the formation of echo chambers and exacerbate societal polarization. The analyses extend beyond this scope, utilizing the introduced key elements related to opinion dynamics and structural insights to produce a comprehensive discourse fragmentation analysis. The framework's ability to identify and track discourse fragmentation provides critical insights for misinformation risk assessment, enabling early detection of false narrative communities and their evolution patterns.