A significant problem we face today is increasingly volatile, uncertain, and complex political-military strategic environments, where pacing, peer adversaries employing gray-zone tactics hold our national resources, instruments of power (i.e. Diplomatic, Information, Military, Economic (DIME)), and emerging technologies at risk. Many of these tactics and “soft-power” capabilities, are non-physics-based, fuzzy-logic models, and have been cost-prohibitive to produce or lack data to inform. Furthermore, our adversaries act in locations where journalists are our only situational awareness mechanism to inform decisions. Journalism could offer unique perspectives to inform statistical insight into world events through comprehensive recognition of routine DIME patterns across multiple domains over time. This data can assist in distinguishing pattern anomalies (e.g. behaviors the world press identifies as coercive) to provide timely event detection, identification, classification, and alerting to inform decision-makers’ understanding of interdependencies across Political, Military, Economic, Social, Information, Infrastructure, Physical environment, and Time (PMESII-PT) aspects. Based on positive interest and feedback from the I/ITSEC 2023 paper review sub-committee, we present a follow-on to our paper “Evaluation of Open-Source Data for Gray-zone Operations Decision-Systems” (Ducharme et al).  Specifically, this paper will examine feeding the results of GDELT newsfeeds into 5-dimensional statistical DIME space + time as a basis for cost-benefit-risk calculations in the gray-zone arena. There are four facets to this proposed approach.  First, the creation of more topic models and training them against 7 years of historical data. Second, further expansion of our thematic ruleset for both coercive behaviors as well as some expansion to normal international relations for analytical context. Thirdly, analysis of the 7-year timeline for identification of the most significant behavioral patterns journalists have identified. Finally, we will discuss how our created framework and topical modeling, expanding to a larger historical sample, as it applies to feed a decision-support system (DSS) (e.g. operational, wargames, etc.).