DiEvD-SF
Disruptive Event Detection Using Continual Machine Learning With Selective Forgetting
Bibliographic Data
| ID | 22107648 |
|---|---|
| Authors | Aditi Seetha (0000-0002-3105-8577, Malaviya National Institute of Technology Jaipur), Satyendra Singh Chouhan (0000-0002-0280-7364, Malaviya National Institute of Technology Jaipur), Emmanuel S Pilli (0000-0002-6056-1147, Malaviya National Institute of Technology Jaipur), Vaskar Raychoudhury (0000-0003-3722-6954, Miami University), Snehanshu Saha (0000-0002-8458-604X, Birla Institute of Technology and Science, Pilani - Goa Campus) |
| Year | 2024 |
| Volume | 11 |
| Issue | 3 |
| Pages | 4189-4201 |
| Publication date | 2024-06-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | IEEE Transactions on Computational Social Systems (JOURNAL) |
| Journal identifiers | ISSN: 2329-924X • E-ISSN: 2373-7476 |
| Publisher | Institute of Electrical and Electronics Engineers (IEEE) (PUBLISHER) |
| DOI | 10.1109/tcss.2024.3364544 |
| OpenAlex | W4392543910 |
| Language | EN |
| References cited | 33 |
Detecting disruptive events (DEs), such as riots, protests, and natural calamities, from social media is essential for studying geopolitical dynamics. To automate the process, existing methods rely on classical machine learning (ML) models applied to static datasets, which is counterproductive. To detect DEs from dynamic data streams, this article introduces a novel DiEvD-SF framework, which uses continual machine learning (CML) with selective forgetting. Twitter (currently “X”) is used as a real-time and dynamic data source for validation. DiEvD-SF considers the temporal nature of DEs and “selectively forgets” outdated DEs through machine unlearning. To the best of our knowledge, this article is the first to apply CML with selective forgetting to discard outdated DEs and to continue learning about the new DEs. Extensive evaluation using a painstakingly collected Twitter dataset shows that the proposed framework continually identifies new DEs with an average incremental accuracy of 78.942% and successfully forgets old DEs with an average forgetting time of 118.498 seconds, which is better than the state-of-the-art. Additionally, computational analysis is performed to establish the effectiveness of the DiEvD-SF framework by applying various candidate selection strategies
Cognitive psychology · Forgetting · Machine learning · Advanced Malware Detection Techniques · Anomaly Detection Techniques and Applications · Computer Science · Network Security and Intrusion Detection · Psychology · Artificial Intelligence
| Highly cited | No |
|---|