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Kolaee Darabi, Khalil, Hassanpour, Hamid, Sheikhahmadi, Amir. (1404). Content-Based Event Detection on X (Twitter): A Survey of NLP Techniques for Sub-Event Prediction and Evolution. پژوهشنامه مدیریت اجرایی, 3(2), 11-28. doi: 10.22080/cste.2025.29964.1081
Khalil Kolaee Darabi; Hamid Hassanpour; Amir Sheikhahmadi. "Content-Based Event Detection on X (Twitter): A Survey of NLP Techniques for Sub-Event Prediction and Evolution". پژوهشنامه مدیریت اجرایی, 3, 2, 1404, 11-28. doi: 10.22080/cste.2025.29964.1081
Kolaee Darabi, Khalil, Hassanpour, Hamid, Sheikhahmadi, Amir. (1404). 'Content-Based Event Detection on X (Twitter): A Survey of NLP Techniques for Sub-Event Prediction and Evolution', پژوهشنامه مدیریت اجرایی, 3(2), pp. 11-28. doi: 10.22080/cste.2025.29964.1081
Kolaee Darabi, Khalil, Hassanpour, Hamid, Sheikhahmadi, Amir. Content-Based Event Detection on X (Twitter): A Survey of NLP Techniques for Sub-Event Prediction and Evolution. پژوهشنامه مدیریت اجرایی, 1404; 3(2): 11-28. doi: 10.22080/cste.2025.29964.1081

Content-Based Event Detection on X (Twitter): A Survey of NLP Techniques for Sub-Event Prediction and Evolution

Contributions of Science and Technology for Engineering
مقاله 2، دوره 3، شماره 2، اردیبهشت 2026، صفحه 11-28    اصل مقاله (879.89 K)
نوع مقاله: Original Article
شناسه دیجیتال (DOI): 10.22080/cste.2025.29964.1081
نویسندگان
Khalil Kolaee Darabi1؛ Hamid Hassanpour* 2؛ Amir Sheikhahmadi1
1Department of Computer Engineering, Sa.C., Islamic Azad University, Sanandaj, Iran
2Shahrood University of Technology
تاریخ دریافت: 09 شهریور 1404،  تاریخ بازنگری: 01 آبان 1404،  تاریخ پذیرش: 28 آبان 1404 
چکیده
The rapid growth of Twitter has transformed it into a critical real-time sensor for world events, often surfacing information about disasters, political upheaval, and public health crises ahead of traditional sources. While detecting major events is valuable, the ability to identify sub-events—fine-grained, evolving components—is crucial for deeper situational awareness. This survey provides a comprehensive review of NLP techniques for sub-event prediction and evolution on Twitter. We introduce a novel taxonomy that categorizes methods from traditional text-based and graph-based approaches to modern deep learning and transformer-based architectures, specifically evaluating their capacity to capture sub-event dynamics. Our analysis covers widely used benchmarks (e.g., CrisisNLP, CrisisBench, COVID-Twitter datasets) and evaluation protocols (e.g., precision, recall, F1, clustering metrics). The findings indicate that while significant advances have been made, the fusion of multimodal data, the application of large language models, and the adoption of privacy-preserving frameworks like federated learning represent the most promising pathways for robust sub-event detection. By synthesizing methodological advances and evaluation practices, this paper underscores the central role of sub-event analysis in advancing research and its critical importance for real-time, high-stakes applications in disaster response, public health, and security.
کلیدواژه‌ها
Event Detection؛ Deep Learning؛ Sub-event Prediction؛ Social Media Analysis؛ Natural Language Processing؛ Content-based Methods
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