###### Abstract
This is particularly relevant in the context of the Future Internet, where the volume of data generated online is expected to grow exponentially, necessitating advanced tools for data analysis and threat detection. The framework can be integrated into cybersecurity systems to monitor and analyze online communications for signs of radicalization or terrorist activity. This is crucial for preventing cyberattacks that may be orchestrated by terrorist groups, such as hacking, data breaches, or the spread of malicious content. Owing to the great success of distributed word representation, distributed word representations along with other relevant features were used as multiple types of word embedding and were shown to positively contribute to several NLP tasks such as sentiment classification, rhetorical analysis, and predicting the education status of children's essays to word embedding algorithms [6; 7]. Among the studies associated with terrorism, the structure and dynamics of alliances among terrorist organizations have been emphasized, utilizing complex network theory to analyze the relationships and cooperation patterns between these groups [8]. This pertains to the evolution of networks and the identification of influential groups within them. Complex network theory facilitates the comprehension of terrorist alliance networks derived from empirical evidence. [PERSON] et al. [9] identified the pivotal entities inside terrorist alliance networks, whose elimination or disruption would significantly affect the network's structure and performance. Certain terrorist organizations can be recognized as pivotal nodes within the network, signifying their role as main connectors or hubs within the alliance. The historical progression of terrorism into specific phases, employing a time series-based methodology for complicated network analysis, is essential [10]. This will facilitate an understanding of the evolution of terrorist networks and methods across time,offering insights into the adaptive characteristics of these groups and delineating several epochs of terrorism. Considering that terrorism is of interest to researchers, and given that its predictions have been far from accurate, coupled with the frustration of slow and effective responses, it is often ultimately characterized by 'boots on the ground' [11]. This frustration is particularly relevant in the 'information age'. That is why this current study establishes social connections and online behavior that would be able to harness some predictive capability. The justification of this dwells on the fact that studies on terrorism emanating from social and cognitive psychology and on multi-agent killing and other intercultural conflicts have also shown that the individual and situational determinants of behavior are associated with information and communication technology. To make the discriminating elements of this evident for the development of predictive models that look solely at terrorism is an important antecedent. Language is an important mark and record of human cognition. In interventions, the use of language has a marked impact on changing behavior [12]. Thus, there has been a move to data mine the web as a source of predictive capability, and the same applies to counterterrorism. That is why this paper contributes in the following ways:  
* The research demonstrated that co-occurrence is evident, as the \"lexical patterns\" anticipated within any textual context exhibit similarities. Subsequently, it developed a synthesis of multiple lexical data sources related to terrorism and generated various multi-lexical data sources to evaluate their applicability. * The research suggests that the Extra Trees model achieved the highest predictive accuracy for lexical patterns in the context of terrorism-related text prediction. This was the case when the model was implemented.