An evaluation of the run-time and task-based performance of event detection techniques for Twitter. (December 2016)

• Record Type: Journal Article
• Authors: Weiler, Andreas; Grossniklaus, Michael; Scholl, Marc H.
• Is Part Of: Information systems. Volume 62(2016)
• Page Start: 207
• Page End: 219
• Publication Date: 2016-12
• Subjects: Event detection -- Performance evaluation -- Twitter social media data stream; Database management -- Periodicals; Electronic data processing -- Periodicals; Bases de données -- Gestion -- Périodiques; Informatique -- Périodiques; Database management; Electronic data processing; Periodicals; 005.7
• DOI: 10.1016/j.is.2016.01.003
• Languages: English
• ISSNs: 0306-4379

Abstract

Abstract: Twitter׳s increasing popularity as a source of up-to-date news and information about current events has spawned a body of research on event detection techniques for social media data streams. Although all proposed approaches provide some evidence as to the quality of the detected events, none relate this task-based performance to their run-time performance in terms of processing speed, data throughput, or memory usage. In particular, neither a quantitative nor a comparative evaluation of these aspects has been performed to date. In this article, we study the run-time and task-based performance of several state-of-the-art event detection techniques for Twitter. In order to reproducibly compare run-time performance, our approach is based on a general-purpose data stream management system, whereas task-based performance is automatically assessed based on a series of novel measures. Abstract : Highlights: Streaming implementations of state-of-the-art event detection techniques for Twitter. Study of the task-based and run-time performance of event detection techniques. Scalable measures to assess performance of event detection techniques automatically. Platform-based approach to enable further performance studies for future techniques.