Sep 2026· Zenodo (CERN European Organization for Nuclear Research)
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.
The results are packaged in the Greenfield Startup Model (GSM), which explains the priority of startups to release the product as quickly as possible, and the need to shorten time-to-market, by speeding up the development through low-precision engineering activities.
Carmine Giardino, Nicolò Paternoster, M. Unterkalmsteiner et al.· IEEE Transactions on Softwar...· 178 citations· ⚡14
Software startup companies develop innovative, software-intensive products within limited timeframes and with few resources, searching for sustainable and scalable business models.
M. Unterkalmsteiner, P. Abrahamsson, Xiaofeng Wang et al.· e-Informatica Software Engin...· 157 citations· ⚡17
This study conducts a case survey study based on the secondary data of the major pivots happened in 49 software startups, and demonstrates that customer need pivot is the most common among all pivot types.
Sohaib Shahid Bajwa, Xiaofeng Wang, Anh Nguyen-Duc et al.· Empirical Software Engineeri...· 127 citations· ⚡15
The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.
Nattakarn Phaphoom, Xiaofeng Wang, S. Samuel et al.· Journal of Systems and Softw...· 111 citations· ⚡8
The ongoing work building a Raspberry Pi cluster consisting of 300 nodes is presented, with potential use cases being an inexpensive and green test bed for cloud computing research and a robust and mobile data center for operating in adverse environments.
P. Abrahamsson, S. Helmer, Nattakarn Phaphoom et al.· IEEE International Conferenc...· 110 citations· ⚡7
The results indicate that software developers are a slightly happy population, but the need for limiting the unhappiness of developers remains, and 219 factors representing causes of unhappiness while developing software are identified.
D. Graziotin, Fabian Fagerholm, Xiaofeng Wang et al.· International Conference on...· 84 citations· ⚡6
Related blog posts
MIT News · Artificial Intelligence· news.mit.eduSep 14, 2026
The “HardFlow” algorithm could help generative AI models produce high-quality outputs that obey strict requirements when “pretty close” doesn’t cut it.
AI may appear weightless, but every model depends on physical infrastructure. To understand responsible AI, we need to look beyond algorithms and consider the entire lifecycle of the hardware behind them. The post Responsible AI Must Consider Its Afterlife appeared first on GPT-Lab.