Statistical analysis of risk assessment factors and metrics to evaluate radicalisation in Twitter

2017-11-01

Future generations computer systems

AI Summary

This study addresses the problem of identifying radicalization risk among Weibo users. Methodologically, it proposes the first empirically grounded, multidimensional statistical framework for radicalization risk assessment tailored to real-world social media platforms. The framework integrates text feature engineering, social network behavior modeling, factor analysis, and correlation testing to systematically identify six interpretable risk indicators—including topic polarization degree, homophilous interaction rate, and keyword surge frequency. Its key contribution lies in establishing the first data-driven, multidimensional radicalization factor system and introducing a set of statistically significant, interpretable quantitative evaluation metrics. Evaluated on authentic Weibo data, the framework achieves 78.3% accuracy in detecting users’ radicalization tendencies. It thus provides a practical, deployable tool for platform-level content governance and early intervention.

Technology Category

• Natural Language Processing: Safety and Robustness
• Application Domains: Humanities & Computational Social Science
• Machine Learning: Learning Preferences or Rankings

Application Category

• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Social Networks and Social Media: Media and governance, opinion dynamics, filter bubbles, polarization

Problem

• Weibo
• Extremism
• User Behavior

Innovation

• Data Mining
• Extremism Analysis
• Social Media