## 5 Discussions
On the other hand, if to some limited extent a question of \"How can lexical patterns be psychosocially interpreted?\" offered an interpretation of the theory, it can be extended further based on symbolic interactionist perspectives that explain elements of processes and general attitudes. However, lexically, this research combined the best results from the open data sources dataset and GTD dataset and showcases the potential of leveraging lexical patterns in predictive terrorism models. This study employed five supervised machine learning classifiers to predict lexical patterns from multi-lexical data sources associated with terrorism. By evaluating the performance of these models across multiple metrics--accuracy, precision, recall, F1-score, and ROC-AUC--the research identifies the most suitable approach for detecting terrorism-related terms in unstructured text. The models were tested on two datasets: an open-source text corpus and the Global Terrorism Database (GTD). Through extensive experimentation and post-hoc hyperparameter optimization, consistent performance trends emerged. Among the evaluated models, XGBoost consistently demonstrated the most balanced and robust performance across all critical metrics. Although Extra Trees achieved the highest overall accuracy (94.31% on the open dataset and 94.22% on GTD), this was primarily due to its conservative classification approach, which led to a high number of true negatives. This behavior resulted in relatively lower recall values (71.97% and 74.11%, respectively), which is a concern in applications where missing relevant instances (false negatives) has serious implications. In contrast, XGBoost attained a significantly higher recall (81.32% and 82.52%) and F1-score (0.8721 and 0.8784) across both datasets, underscoring its superior capacity to identify nuanced patterns and minimize false negatives. XGBoost's gradient boosting framework contributes to its predictive strength by iteratively correcting classification errors through additive modeling. This capability allows the model to adaptively learn complex feature interactions and lexical dependencies within high-dimensional TF-IDF representations. Such strengths are particularly valuable in terrorism detection, where the co-occurrence of semantically ambiguous terms and class imbalance can obscure key indicators. In both experimental scenarios, XGBoost offered a consistent trade-off between sensitivity and specificity, making it a better fit for high-stakes intelligence applications than models optimized solely for accuracy. It effectively captured subtle contextual indicators embedded in natural language, which is crucial for detecting emerging threats or radicalization cues. The model's adaptability and scalability further affirm its suitability for real-time implementation in cyber-intelligence systems. While random forest and Bagging models also showed balanced performance, they lacked the iterative refinement mechanism of boosting. Extra Trees, although efficient and computationally attractive, prioritized a conservative classification that could limit its practical use in sensitive threat detection environments. Decision Trees remained interpretable but demonstrated the weakest performance in recall, making them suboptimal for complex, ambiguous textual classification tasks. In conclusion, the evidence across datasets and evaluation phases strongly supports the adoption of XGBoost as the preferred classifier for terrorism-related lexical pattern detection. Its high recall and F1-score, coupled with strong precision, make it a reliable model for operational deployment in cyber-threat surveillance and linguistic intelligence. Future work may further enhance this framework by integrating XGBoost with deep learning-based semantic models and applying it in multilingual or multimodal terrorism datasets for broader generalizability. The integration of linguistic analysis into intelligence may become a supplementary feature for communication surveillance.