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This content can be combined and processed to detect events, entities and popular moods to feed various new large-scale data-analysis applications. On the downside, these content items are very noisy and highly informal, making it difficult to extract sense out of the stream. Taking to account all the difficulties, we propose a new event detection approach combining linguistic features and Twitter features. Finally, we present our system that aims (1) detect new events, (2) to recognize temporal markers pattern of an event, (3) and to classify important events according to thematic pertinence, author pertinence and tweet volume.<\/jats:p>","DOI":"10.4018\/ijksr.2017070104","type":"journal-article","created":{"date-parts":[[2017,6,19]],"date-time":"2017-06-19T16:12:48Z","timestamp":1497888768000},"page":"54-67","source":"Crossref","is-referenced-by-count":1,"title":["Leveraging Temporal Markers to Detect Event from Microblogs"],"prefix":"10.4018","volume":"8","author":[{"given":"Soumaya","family":"Cherichi","sequence":"first","affiliation":[{"name":"University of Tunis, Higher Institute of Management, Tunis, Tunisia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rim","family":"Faiz","sequence":"additional","affiliation":[{"name":"University of Carthage, Institute of Business Studies, Tunis, Tunisia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2432","reference":[{"key":"IJKSR.2017070104-0","first-page":"487","article-title":"Fast Algorithms for Mining Association Rules.","author":"R.Agrawal","year":"1994","journal-title":"Proceedings of the 20th International Conference on Very Large Databases"},{"key":"IJKSR.2017070104-1","doi-asserted-by":"publisher","DOI":"10.1145\/1242572.1242802"},{"key":"IJKSR.2017070104-2","doi-asserted-by":"publisher","DOI":"10.1145\/2124295.2124360"},{"key":"IJKSR.2017070104-3","doi-asserted-by":"crossref","unstructured":"Cherichi, S., & Faiz, R. 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