Aditi Seetha;Satyendra Singh Chouhan;Emmanuel S. Pilli;Vaskar Raychoudhury;Snehanshu Saha
{"title":"DiEvD-SF: Disruptive Event Detection Using Continual Machine Learning With Selective Forgetting","authors":"Aditi Seetha;Satyendra Singh Chouhan;Emmanuel S. Pilli;Vaskar Raychoudhury;Snehanshu Saha","doi":"10.1109/TCSS.2024.3364544","DOIUrl":null,"url":null,"abstract":"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 \n<italic>DiEvD-SF</i>\n 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. \n<italic>DiEvD-SF</i>\n considers the temporal nature of DEs and “selectively forgets” outdated DEs through machine unlearning. To the best of our knowledge, this article is the first to apply CML with selective forgetting to discard outdated DEs and to continue learning about the new DEs.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 \n<italic>DiEvD-SF</i>\n framework by applying various candidate selection strategies.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":null,"pages":null},"PeriodicalIF":4.5000,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Computational Social Systems","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10462483/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, CYBERNETICS","Score":null,"Total":0}
引用次数: 0
Abstract
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.
期刊介绍:
IEEE Transactions on Computational Social Systems focuses on such topics as modeling, simulation, analysis and understanding of social systems from the quantitative and/or computational perspective. "Systems" include man-man, man-machine and machine-machine organizations and adversarial situations as well as social media structures and their dynamics. More specifically, the proposed transactions publishes articles on modeling the dynamics of social systems, methodologies for incorporating and representing socio-cultural and behavioral aspects in computational modeling, analysis of social system behavior and structure, and paradigms for social systems modeling and simulation. The journal also features articles on social network dynamics, social intelligence and cognition, social systems design and architectures, socio-cultural modeling and representation, and computational behavior modeling, and their applications.