Towards Reproducible Research of Event Detection Techniques for Twitter
Towards Reproducible Research of Event Detection Techniques for Twitter
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DOI：
10.1109/sds.2019.000-5
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发表时间：
2019-06
期刊：
影响因子：
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通讯作者：
Andreas Weiler;Harry Schilling;L. Kircher;Michael Grossniklaus
中科院分区：
文献类型：
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作者：
Andreas Weiler;Harry Schilling;L. Kircher;Michael Grossniklaus
许多研究领域的一个主要挑战是实现、实验或评估的可重复性。新的数据来源和研究方向使可重复性变得更加复杂。例如，Twitter作为最新新闻和信息的来源继续受到欢迎。因此，人们提出了许多事件检测技术来科普社交媒体数据流稳定增长的速度和数量。虽然其中一些作品提供了他们的实施或进行评估所提出的技术，它几乎是不可能重现他们的实验。主要缺点是Twitter禁止发布研究人员在实验中使用的抓取数据集。在这项工作中，我们提出了一个调查的巨大景观的实施，实验和评估所提出的不同的研究工作。此外，我们提出了一个再现性工具包，包括Twistor（Twitter流模拟器），它可以用来模拟人工Twitter数据流（包括事件）作为输入的实验或事件检测技术的评估。我们进一步提出了国家的最先进的事件检测技术的再现性工具包的实验应用。
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 state-of-the-art event detection techniques.