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Hassanpour, Hamid, Kolaee, Khalil, Sheikhahmadi, A.. (1404). Content-Based Event Detection on X (Twitter): A Survey of NLP Techniques for Sub-Event Prediction and Evolution. پژوهشنامه مدیریت اجرایی, (), -. doi: 10.22080/cste.2025.29964.1081
Hamid Hassanpour; Khalil Kolaee; A. Sheikhahmadi. "Content-Based Event Detection on X (Twitter): A Survey of NLP Techniques for Sub-Event Prediction and Evolution". پژوهشنامه مدیریت اجرایی, , , 1404, -. doi: 10.22080/cste.2025.29964.1081
Hassanpour, Hamid, Kolaee, Khalil, Sheikhahmadi, A.. (1404). 'Content-Based Event Detection on X (Twitter): A Survey of NLP Techniques for Sub-Event Prediction and Evolution', پژوهشنامه مدیریت اجرایی, (), pp. -. doi: 10.22080/cste.2025.29964.1081
Hassanpour, Hamid, Kolaee, Khalil, Sheikhahmadi, A.. Content-Based Event Detection on X (Twitter): A Survey of NLP Techniques for Sub-Event Prediction and Evolution. پژوهشنامه مدیریت اجرایی, 1404; (): -. 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
مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 28 آبان 1404
نوع مقاله: Original Article
شناسه دیجیتال (DOI): 10.22080/cste.2025.29964.1081
نویسندگان
Hamid Hassanpour* 1؛ Khalil Kolaee2؛ A. Sheikhahmadi2
1Shahrood University of Technology
2SA
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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