Survey and Experimental Analysis of Event Detection Techniques for Twitter. (1st March 2017)

• Record Type: Journal Article
• Authors: Weiler, Andreas; Grossniklaus, Michael; Scholl, Marc H.
• Is Part Of: Computer journal. Volume 60:Number 3(2017)
• Page Start: 329
• Page End: 346
• Publication Date: 2017-03-01
• Subjects: performance evaluation -- event detection -- Twitter data streams; Computers -- Periodicals; 005.1
• DOI: 10.1093/comjnl/bxw056
• Languages: English
• ISSNs: 0010-4620

Abstract

Abstract: Twitter's popularity as a source of up-to-date news and information is constantly increasing. In response to this trend, numerous event detection techniques have been proposed to cope with the rate and volume of Twitter data streams. Although most of these works conduct some evaluation of the proposed technique, a comparative study is often omitted. In this paper, we present a survey and experimental analysis of state-of-the-art event detection techniques for Twitter data streams. In order to conduct this study, we define a series of measures to support the quantitative and qualitative comparison. We demonstrate the effectiveness of these measures by applying them to event detection techniques as well as to baseline approaches using real-world Twitter streaming data.