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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.<\/jats:p>","DOI":"10.1177\/0165551518785548","type":"journal-article","created":{"date-parts":[[2018,7,3]],"date-time":"2018-07-03T07:07:27Z","timestamp":1530601647000},"page":"156-168","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":7,"title":["A comprehensive study of online event tracking algorithms in social networks"],"prefix":"10.1177","volume":"45","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8640-1267","authenticated-orcid":false,"given":"Mahsa","family":"Seifikar","sequence":"first","affiliation":[{"name":"Department of Artificial Intelligence, Faculty of Computer Engineering, K. N. 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