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Eng."],"published-print":{"date-parts":[[2021,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Twitter is one of the most popular micro-blogging and social networking platforms where users post their opinions, preferences, activities, thoughts, views, etc., in form of tweets within the limit of 280 characters. In order to study and analyse the social behavior and activities of a user across a region, it becomes necessary to identify the location of the tweet. This paper aims to predict geolocation of real-time tweets at the\u00a0city level collected for a period of 30\u00a0days by using a combination of convolutional neural network and a bidirectional long short-term memory by extracting features within the tweets and features associated with the tweets. We have also compared our results with previous baseline models and the findings of our experiment show a significant improvement over baselines methods achieving\u00a0an accuracy of 92.6 with\u00a0a median error of 22.4\u00a0km at city level prediction.<\/jats:p>","DOI":"10.1007\/s41019-021-00165-1","type":"journal-article","created":{"date-parts":[[2021,7,8]],"date-time":"2021-07-08T07:02:43Z","timestamp":1625727763000},"page":"402-410","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["Predicting Geolocation of Tweets: Using Combination of CNN and BiLSTM"],"prefix":"10.1007","volume":"6","author":[{"given":"Rhea","family":"Mahajan","sequence":"first","affiliation":[]},{"given":"Vibhakar","family":"Mansotra","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,7,8]]},"reference":[{"key":"165_CR1","doi-asserted-by":"publisher","DOI":"10.1007\/s41109-019-0134-3","author":"L Luceri","year":"2019","unstructured":"Luceri L, Braun T, Giordano S (2019) Analyzing and inferring human real-life behavior through online social networks with social influence deep learning. 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