## 4 Experimental Analysis
### Presentation of Analysis on a Practical Application Related to Human-Machine Interaction  
This paper's framework can be adapted for real-time analysis, where machine learning models continuously monitor online content and provide alerts to human analysts when potential threats are detected. This real-time interaction between humans and machines is essential for proactive threat detection and response, which is a key requirement in the Future Internet. A typical scenario is presented in Table 11. This is a case where a practical application of this research relates to human-machine interaction. The predefined TF-IDF score maps with the study's results determine whether a given text should be \"automatically flagged as suspicious\" or \"not\". High TF-IDF scores (above 0.06) are flagged as they strongly relate to known threat keywords. Hence, the co-occurrence with threat-related terms increases the likelihood of automatic flagging; eventually, the Machine Learning Predictions: Models with higher accuracy (Extra Trees, XGBoost) are more likely to flag content if it matches known patterns. Based on the study's findings and model predictions, flagged and non-flagged content \"Bombing\" (Flagged as Suspicious)\" has a high TF-IDF score, meaning it appears frequently in terrorism-related content (see Table 11). It is also strongly associated with words like \"Explosion\" and \"Attack\", which indicate violence or criminal intent. Security Checkpoint\" is not flagged, even though it relates to defensive measures rather than offensive or terrorist activities. While it co-occurs with words like \"Surveillance\" and \"Monitor\", these are neutral or security-related terms. Another case where the practical application of this research relates to human-machine interaction is in associating online \"user conversations\" with the study's results. This can be determined whether an \"AI-powered chatbot\" should or \"any monitoring interface\" can be flagged up or not. A typical case to an AI-powered chatbot is presented in Table 12. Flags tagging for \"Provide de-radicalization content (mild warning, education, intervention)\", \"Redirect to human counselors (high risk, immediate attention needed)\", and \"No intervention (conversation is neutral or non-threatening)\" are defined in order to establish an AI-powered chatbot response to radicalization indicators. This system can ensure that an AI-powered chatbot can \"Detect early signs of radicalization and prevent escalation\", \"Offer soft interventions through education and alternative perspectives\", or \"Escalate severe cases to human experts before a threat manifests\". The statement \"They will pay for this injustice!\" (Redirect to Human Counselors) expresses anger and intent for retribution, indicating a potential escalation toward violence. The co-occurrence of \"revenge\" and \"attack\" aligns with high-risk radical speech patterns found in extremist narratives. In the AI chatbot case, it flags this as a serious case and redirects the user to a human counselor for immediate intervention. This dwells on the Extra Trees (94.31% accuracy) of prediction (see Table 12). Similarly, a \"Government surveillance is too much\" (no intervention). This message expresses concern about government surveillance, which is a common civil rights issue, with no direct call to violence, extremism, or radicalization. The words \"Privacy\" and \"Freedom\" are frequently used in legitimate political discussions rather than extremist rhetoric. The chatbot does not intervene since the message is within normal discourse. Hence, it established a practical application related to human-machine interaction to ensure a balanced approach between