Event detection in social media: A survey

Grossniklaus

Scholl

2016

The Computer Journal

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.

Section: Introductionmentioning

confidence: 99%

Editorial: Survey and Experimental Analysis of Event Detection Techniques for Twitter

Grossniklaus

Scholl

2016

The Computer Journal

“…As extensions of our evaluations we plan to include further parameter settings and to research the interdependencies of the parameters. By reviewing the surveys of related work (e.g., Nurwidyantoro and Winarko [20], Madani et al [15], or Farzindar and Khreich [10]), we found several candidates for this venture. On the one hand, techniques such as TwitterMonitor [16] and Twevent [13] are interesting because the techniques they use are closely related to our own techniques.…”

Section: Discussionmentioning

confidence: 99%

Stability Evaluation of Event Detection Techniques for Twitter

Lecture Notes in Computer Science

Beel

Gipp

et al. 2016

Abstract. Twitter continues to gain popularity as a source of up-todate news and information. As a result, numerous event detection techniques have been proposed to cope with the steadily increasing rate and volume of social media data streams. Although most of these works conduct some evaluation of the proposed technique, comparing their effectiveness is a challenging task. In this paper, we examine the challenges to reproducing evaluation results for event detection techniques. We apply several event detection techniques and vary four parameters, namely time window (15 vs. 30 vs. 60 mins), stopwords (include vs. exclude), retweets (include vs. exclude), and the number of terms that define an event (1...5 terms). Our experiments use real-world Twitter streaming data and show that varying these parameters alone significantly influences the outcomes of the event detection techniques, sometimes in unforeseen ways. We conclude that even minor variations in event detection techniques may lead to major difficulties in reproducing experiments.

“…Over the last years, Twitter gained significant importance for researchers, especially in the context of event detection techniques applied to social media data streams. The great variety of event detection techniques for Twitter is also reflected in a couple of recently presented surveys [6], [9], [18], [23], [36]. At the same time and with the same variety, methods to evaluate the results of event detection techniques for Twitter appeared.…”

Section: Issues Of Reproducibiltymentioning

confidence: 99%

Towards Reproducible Research of Event Detection Techniques for Twitter

2019 6th Swiss Conference on Data Science (SDS)

Schilling

Kircher

et al. 2019

A major challenge in many research areas is reproducibility of implementations, experiments, or evaluations. New data sources and research directions complicate the reproducibility even more. For example, Twitter continues to gain popularity as a source of up-to-date news and information. As a result, numerous event detection techniques have been proposed to cope with the steadily increasing rate and volume of social media data streams. Although some of these works provide their implementation or conduct an evaluation of the proposed technique, it is almost impossible to reproduce their experiments. The main drawback is that Twitter prohibits the release of crawled datasets that are used by researchers in their experiments. In this work, we present a survey of the vast landscape of implementations, experiments, and evaluations presented by the different research works. Furthermore, we propose a reproducibility toolkit including Twistor (Twitter Stream Simulator), which can be used to simulate an artificial Twitter data stream (including events) as input for the experiments or evaluations of event detection techniques. We further present the experimental application of the reproducibility toolkit to stateof-the-art event detection techniques.