Efficient Event Detection for the Blogosphere

Hennig, Patrick; Berger, Philipp; Kurzynski, Daniel; Rantzsch, Hannes; Meinel, Christoph

doi:10.1109/bdcloud.2014.67

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Supervised Methods For Trend Detection1

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Cited by 3 publications

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“…Hennig et al [42] have focused in 2014 on extracting, analysing and visualising events whose information are found in a lot of documents in blogs. This work detects all events on a set of documents obtained during a given time interval.…”

Section: Supervised Methods For Trend Detectionmentioning

confidence: 99%

A comprehensive study of online event tracking algorithms in social networks

Seifikar

Farzi

2018

Journal of Information Science

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Recently, social networks have provided an important platform to detect trends of real-world events. The trends of real-world events are detected by analysing flow of massive bulks of data in continuous time steps over various social media platforms. Today, many researchers have been interested in detecting social network trends, in order to analyse the gathered information for enabling users and organisations to satisfy their information need. This article is aimed at complete surveying the recent text-based trend detection approaches, which have been studied from three perspectives (algorithms, dimension and diversity of events). The advantages and disadvantages of the considered approaches have also been paraphrased separately to illustrate a comprehensive view of the previous works and open problems.

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Section: Supervised Methods For Trend Detectionmentioning

confidence: 99%