This paper presents a scalable framework for assessing threatening online communication using eight theoretically grounded warning indicators. These indicators are extracted through a hybrid approach that integrates dictionary-based methods, machine learning classifiers, and large language models. Our findings show a statistically significant correlation between the presence of these indicators and expert assessments of individuals deemed to pose a risk of targeted violence. This underscores the potential of the proposed indicators to support reliable and efficient threat assessment in digital environments.