Evaluation Measures for Event Detection Techniques on Twitter Data Streams

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 social media 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 series of measures that we designed to support the quantitative and qualitative comparison of event detection techniques. In order to demonstrate the effectiveness of these measures, we apply them to state-of-the-art event detection techniques as well as baseline approaches using real-world Twitter streaming data.