Multi-platform analysis of sexual harassment networks: gender dynamics and digital amplification

This study examines digital sexual harassment as a cross-platform phenomenon that amplifies gender-based violence through interconnected social networks. The study develops a comprehensive theoretical framework by combining Social Network Analysis (SNA) and gender-sensitive approaches to identify structural patterns, bridge actors, and amplification mechanisms. The analysis was conducted on 57,514 interactions from 2,072 unique actors on Platform X (Twitter) and YouTube using NodeXL Pro and Python. Community detection was performed using the Leiden algorithm, while statistical validation involved Mann–Whitney U tests, Kolmogorov–Smirnov tests, and random models. Qualitative validation, analyzed using the Miles & Huberman framework, was conducted with data from 15 key informants across four stakeholder groups, demonstrating inter-coder reliability of 89.2%. The results show that 73% of central positions on X are held by masculine actors, with 47 bridge actors playing important roles in cross-platform information transfer, achieving amplification effects of up to 4.1 times. The network structure formed was non-random (p < 0.001) and showed systematic gender disparity. These findings confirm that digital sexual harassment operates through cross-platform network structures that marginalize victims' voices, while providing a theoretical basis for designing more gender-equitable intervention strategies and detection systems.