Abstract

Twitter is a popular microblogging and social networking service. Twitter posts are continuously generated and well suited for knowledge discovery using different data mining techniques. We present a novel near real-time approach for processing tweets and detecting events. The proposed method, Multi Timing Chained Windows (MTCW), is independent of the language of the tweets. The MTCW defines several Timing Windows and links them to each other like a chain. Indeed, in this chain, the input of the larger window will be the output of the smaller previous one. Using MTCW, the events can be detected over a few minutes. To evaluate this idea, the required dataset has been collected using the Twitter API. The results of evaluations show the accuracy and the effectiveness of our approach compared with other state-of-the-art methods in the event detection in Twitter.

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Cite this article

@article{cai-4863,
  author  = {Mohammad Mahdi Mojiri and Reza Ravanmehr},
  title   = {Event Detection in Twitter Using Multi Timing Chained Windows},
  journal = {Computing and Informatics},
  volume  = {39},
  number  = {6},
  pages   = {1336--1359},
  year    = {2020},
  issn    = {1335-9150},
  doi     = {10.31577/cai_2020_6_1336}
}
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