Abstract

Strategic Cyber Threat Intelligence (CTI) focuses on highlevel insights, such as identifying targeted industries, attributing attacks to specific ransomware groups, and assessing the scale of data loss. Today, X (formerly Twitter) has become the fastest source for this intelligence, often hosting real-time breach announcements days before formal vendor reports. Converting this raw chatter into actionable intelligence requires navigating a complex linguistic landscape. Conventional Named Entity Recognition (NER) models struggle to parse the informal and highly irregular dialect of social media, creating a blind spot for automated defense systems. To address this challenge, we introduce STINER, a taxonomy and expert-annotated corpus for extracting strategic intelligence from social media streams. We construct a high-quality, expert-annotated dataset of 2,100 real-world alerts and propose a granular taxonomy of eight entity types centered on strategic pivots such as _Threat Actor_ , _Sector_ , and _Location_ . We benchmark nine models across 12 evaluated configurations, spanning general-purpose and domain-adapted encoders, openschema extraction, and generative LLMs in both zero-shot and fine-tuned settings. Domain-adapted encoders such as DarkBERT reach a strict F1score of 89.33%, outperforming both general-purpose baselines and finetuned Large Language Models, which additionally incur substantially higher inference latency. Leveraging STINER-DarkBERT, we conduct a European threat landscape analysis for H1 2025. Our results align with official reporting on major targets while highlighting the distinct visibility profile of attacks in _Spain_ , and illustrate how social-media-driven extraction can surface early signals of the _SafePay_ ransomware campaign prior to its retrospective characterization in vendor threat landscape reports.