Engines of Escalation: How the Hybrid Public Sphere Amplifies Political Violence Targeting Women in Low-Resource Contexts

Engineering notes

Engineering notes will be added by the aipentium editorial team.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为大语言模型、生成式AI、ChatGPT相关技术、计算机视觉、深度学习等高价值论文补充中文说明。

Original abstract

Political Violence Targeting Women (PVTW) has reached record highs in fragile, low-resource settings. This paper presents a structural analysis integrating both theory and empirical evidence to examine how a rapidly evolving hybrid public sphere—where the intertwined logics of traditional news and social media create a bidirectional feedback loop—amplifies hatred and accelerates offline physical harm against women in Nigeria. Adapting the framework of discursive opportunities, we unpack the mechanisms escalating misogyny into violence. We analyze a corpus of over 1.6 billion X (formerly Twitter) posts, traditional news articles, and conflict event data from ACLED. We utilize NaijaXLM-T, a custom Large Language Model tailored to Nigerian text, to accurately measure the visibility (volume) and resonance (intensity) of gender-specific hate, and to extract keywords and topics related to gender-targeting news. Furthermore, we map social interaction networks to isolate 'true contagion' from structural homophily. The hub-and-spoke topology observedamong hateful users serves as our proxy for legitimacy; we posit that hatred disseminated by influential network hubs acts as a digital form of social authorization, actively lowering the friction for offline mobilization. Empirically, we first establish a robust linkage where digital hostility directly catalyzes offline PVTW. Moving beyond this baseline effect, we unpack the structural pathways driving this acceleration by demonstrating how visibility, resonance, and network legitimacy fuel this spillover. We then answer critical questions regarding the differing velocities within this system, detailing the distinct temporal scales at which social media and traditional news amplify offline harm. Acknowledging observational and algorithmic limitations, we interpret these findings as a robust temporal linkage rather than strict causality. Ultimately, this research provides the foundational framework for monitoring and early-warning infrastructures, equipping policymakers and NGOs to prevent enabling stakeholders to pre-position protective resources to prevent digital hate from escalating into lethal PVTW in fragile settings.

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