Cybersecurity and Cyber Intelligence Measures for Monitoring, Preventing, and Mitigating Radicalization Pathways

Social media platforms have reshaped the way users com- municate, providing substantial benefits, but also introduced significant concerns about accelerating ideological enclosure and online radicalization through the combined effects of user behavior, algorithmic personalization, and persuasive actors. This thesis investigates these interlocking mechanisms by de- veloping integrated detection, diagnosis, and mitigation meth- ods that combine deep learning (DL), machine learning (ML), natural language processing (NLP), and network analysis to track radicalization pathways within online environments. Em- pirical evaluations on large, real-world datasets from video- sharing and microblogging platforms demonstrate three prin- cipal findings. First, when behavioral and affective signals are integrated with network representations, they improve the detection and forecasting of echo-chamber persistence, and they correlate strongly with emotional polarization and toxic interactions. Second, a recommender-diversity diagnos- tic identifies latent radicalization risk within recommenda- tion graphs, and an adaptive graph-rewiring policy (DRLGR) reduces exposure to radicalizing pathways while preserving engagement metrics. Third, influential actors exert outsized effects on opinion dynamics: rhetorical alignment with audi- ences magnifies influence beyond direct followers. The thesis contributes (i) a systematic survey and gap analysis, (ii) a be- havioral echo-chamber detection and forecasting framework, (iii) a diagnostic and learned mitigation method for recom- mender systems, and (iv) an integrated model of influencer impact that couples network position and rhetorical strategy.