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

Weiler, Andreas; Grossniklaus, Michael; Scholl, Marc H.

doi:10.1093/comjnl/bxw056

Cited by 19 publications

(34 citation statements)

References 43 publications

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“…The experimental results were reported and the performance of our workflow was evaluated accordingly. Evaluating event detection techniques is a very challenging and complex problem, and there is still no commonly accepted standard [3]. Theoretically, the results of event detection can be evaluated by quantitative and qualitative analysis.…”

Section: Resultsmentioning

confidence: 99%

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Towards Detecting Social Events by Mining Geographical Patterns with VGI Data

Liu

Zhou

Shi

et al. 2018

IJGI

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Detecting events using social media data is important for timely emergency response and urban monitoring. Current studies primarily use semantic-based methods, in which “bursts” of certain semantic signals are detected to identify emerging events. Nevertheless, our consideration is that a social event will not only affect semantic signals but also cause irregular human mobility patterns. By introducing depictive features, such irregular patterns can be used for event detection. Consequently, in this paper, we develop a novel, comprehensive workflow for event detection by mining the geographical patterns of VGI. This workflow first uses data geographical topic modeling to detect the hashtag communities with VGI semantic data. Both global and local indicators are then constructed by introducing spatial autocorrelation measurements. We then adopt an outlier test and generate indicator maps to spatiotemporally identify the potential social events. This workflow was implemented using a real-world dataset (104,000 geo-tagged photos) and the evaluation was conducted both qualitatively and quantitatively. A set of experiments showed that the discovered semantic communities were internally consistent and externally differentiable, and the plausibility of the detected events was demonstrated by referring to the available ground truth. This study examined the feasibility of detecting events by investigating the geographical patterns of social media data and can be applied to urban knowledge retrieval.

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Section: Resultsmentioning

confidence: 99%

“…Currently, most research on event detection with social media uses semantic-based methods and text mining [3,4]. Platforms, such as Twitter, provide accessible streaming data, which contains real-time text information, making it possible to detect emerging social events by examining the change in semantic-based features [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Towards Detecting Social Events by Mining Geographical Patterns with VGI Data

Liu

Zhou

Shi

et al. 2018

IJGI

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“…With this information, the different techniques can be evaluated in a more detailed manner. Since the ground truth only contains events that are known beforehand, it will be interesting to combine these evaluations with studies (e.g., [34]- [36]) that are also allow for serendipitous events. From a technical point of view, it would be a great opportunity to have a framework in which researchers could add their own event detection modules and evaluate their approach against all others.…”

Section: Discussionmentioning

confidence: 99%

“…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

Weiler

Schilling

Kircher

et al. 2019

2019 6th Swiss Conference on Data Science (SDS)

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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.

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“…In this section, we will present some of the related works in event detection from tweets that have motivated us to research in this field. Panagiotou et al (2016), Weiler et al (2016) and Farzindar and Khreich (2015) presented a survey of many approaches that have been used over the past few years for event detection from Twitter. They had also mentioned several open challenges for event detection from tweets.…”

Section: Related Workmentioning

confidence: 99%

Untitled

Morabia¹,

Neti²,

Malapati³

et al. 2019

Proceedings of the 2019 Conference of the North

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Event Detection has been one of the research areas in Text Mining that has attracted attention during this decade due to the widespread availability of social media data specifically twitter data. Twitter has become a major source for information about real-world events because of the use of hashtags and the small word limit of Twitter that ensures concise presentation of events. Previous works on event detection from tweets are either applicable to detect localized events or breaking news only or miss out on many important events. This paper presents the problems associated with event detection from tweets and a tweet-segmentation based system for event detection called SEDTWik, an extension to a previous work, that is able to detect newsworthy events occurring at different locations of the world from a wide range of categories. The main idea is to split each tweet and hash-tag into segments, extract bursty segments, cluster them, and summarize them. We evaluated our results on the well-known Events2012 corpus and achieved state-of-the-art results.

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

Cited by 19 publications

References 43 publications

Towards Detecting Social Events by Mining Geographical Patterns with VGI Data

Towards Detecting Social Events by Mining Geographical Patterns with VGI Data

Towards Reproducible Research of Event Detection Techniques for Twitter

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