## 2 Related Work
[22] utilized spatial and temporal analysis to investigate the impact of alliances and rivalries among terrorist organizations, including al-Qaeda and ISIS, on the frequency and geographical distribution of attacks. The authors utilize statistical modeling to discern trends in near-repeat terrorist action. The research revealed that regional alliances and rivalries considerably influence patterns of terrorist attacks. Alliances resulted in concentrated assaults in certain areas, whereas rivalry frequently scattered attack sites, underscoring the significance of inter-group dynamics in comprehending patterns of terrorist action. [PERSON] et al. [23] employed time-varying causality analysis to examine the correlation between terrorist incidents and variations in oil prices. The article analyzes data over a designated timeframe, investigating the extent and manner in which terrorist incidents influence oil price volatility. The research revealed a varied correlation, wherein terrorist strikes occasionally induce increases in oil prices. This association varies over time, indicating that although terrorism can affect oil prices, this effect is inconsistent and contingent upon certain geopolitical circumstances. [PERSON] and [PERSON] [24] perform an experimental assessment of multiple categorization systems to evaluate their efficacy in forecasting terrorism-related incidents. They evaluate methods such as decision trees, support vector machines, and neural networks on terrorist datasets. Neural networks exhibited superior performance in terrorist prediction, with greater accuracy than alternative methods. The research indicates that machine learning may serve as an effective instrument for predicting terrorist attacks, facilitating preemptive actions. [PERSON] et al. [25] employed quantitative research to categorize worldwide terrorist incidents according to attributes such as geographic location, target type, and methods. The authors employ machine learning techniques to categorize and discern trends in attack data. The research developed a classification system that facilitates the systematic analysis of worldwide terrorism tendencies. Research indicated that assaults differ markedly by geography and target, facilitating more focused strategies for terrorism prevention. [PERSON] et al. [26] investigated the potential correlation between Bitcoin prices and terrorist acts, employing econometric models to assess the influence of terrorism on cryptocurrency price volatility. The study revealed that terrorist acts may induce transient fluctuations in Bitcoin prices, albeit inconsistently. The data indicate a tenuous although discernible connection, as Bitcoin is sporadically employed in the funding of illicit activities, including terrorism. Despite notable advancements in the application of machine learning for terrorism prediction, significant challenges persist. Prior studies, such as those by [PERSON] and [PERSON] [24], demonstrated the efficacy of neural networks and decision trees in identifying terrorism-related incidents, but they often relied on static datasets and failed to account for lexical nuances in open-source textual data. [PERSON] et al. [25] emphasized geographic and categorical trends, yet their models were not optimized for dynamic, real-time inference or textual diversity. Moreover, while multi-label classification models like XRR [21] have shown promise in handling large-scale label sets, they are often tailored to structured domains like recommendation systems and may not generalize effectively to semantically ambiguous, sparse terrorism-related texts. Our study addresses these gaps by proposing a framework that integrates lexical extraction with real-time capable machine learning models. Specifically, we focus on term co-occurrence, TF-IDF weighting, and model fine-tuning to improve precision and recall in terrorism-related term identification--thus responding directly to the limitations identified in earlier works.