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Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community
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Community Shaping in the Digital Age: A Risk-Focused Temporal Fusion Framework for Analyzing Information Diffusion
Amirhossein Dezhboro1, Jose Emmanuel Ramirez-Marquez1, Aleksandra Krstikj2
1Department of Systems Engineering, Schaefer School of Engineering & Science, Stevens Institute of Technology, Hoboken, New Jersey, USA.
Summary
This study introduces a framework to analyze online community dynamics using text and network data. It reveals how real-world events shape online discourse, contributing to echo chambers and polarization.
Area of Science:
- Social Sciences
- Computational Social Science
- Network Science
Background:
- Online communities are central to modern social interaction and information spread.
- Understanding their temporal evolution and dynamics is essential.
- Existing methods often lack a comprehensive approach to analyzing fused text and network data.
Purpose of the Study:
- To develop and validate a framework for analyzing online community dynamics over time.
- To investigate the influence of real-world circumstances on online discourse and community evolution.
- To assess the framework's utility in discourse fragmentation analysis and misinformation risk assessment.
Main Methods:
- Temporal fusion of text and network data from social media platforms.
- Text classification within identified online communities.
- Dynamic social network analysis.
- Evaluation using fourteen key elements derived from social science theories.
Main Results:
- The framework effectively analyzes discourse fragmentation, identifying sexism, racism, xenophobia, ableism, homophobia, and religious intolerance as discourse fragments.
- Online community emergence and dissolution cycles are rapid and reflect discourse fragments.
- Real-world events significantly impact discourse fragments and community structures, potentially fostering echo chambers and polarization.
- The framework enables early detection of false narrative communities and their evolution patterns.
Conclusions:
- The proposed framework offers a robust methodology for discourse fragmentation analysis on social media.
- It provides critical insights into how online environments can exacerbate societal polarization.
- The framework is valuable for misinformation risk assessment and understanding online social dynamics.
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