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Furthermore, extracting geographical information from Arabic tweets is challenging since they contain many nonstandard data (dialects), complex structures, abbreviations, grammatical and spelling mistakes, etc. This study focuses on the localization of Saudi Arabian users who tweet in Arabic. This study proposes a convolutional neural network-based deep learning model to predict a Twitter user\u2019s region-level location using user profiles, text texts, place attachments, and historical tweets. The model was evaluated empirically on a dataset of 95,739 tweets written in Arabic and produced by 4,331 users from Saudi Arabia cities. Regarding classification accuracy, the proposed CNN model outperformed machine learning classifiers such as NB, LR, and SVM with a 60% accuracy on the test set. This study is the first of its kind, aimed at localizing Saudi users based on their tweets.<\/jats:p>","DOI":"10.3233\/jifs-230518","type":"journal-article","created":{"date-parts":[[2023,5,30]],"date-time":"2023-05-30T11:18:32Z","timestamp":1685445512000},"page":"2723-2734","source":"Crossref","is-referenced-by-count":1,"title":["GLDM: Geo-location prediction of twitter users with deep learning methods1"],"prefix":"10.1177","volume":"45","author":[{"given":"Rawabe","family":"Al-Jamaan","sequence":"first","affiliation":[{"name":"College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mourad","family":"Ykhlef","sequence":"additional","affiliation":[{"name":"College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abdulrahman","family":"Alothaim","sequence":"additional","affiliation":[{"name":"College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-230518_ref1","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1007\/978-1-4614-1629-6_9","article-title":"Location -Based Social Networks: Locations","author":"Zheng","year":"2011","journal-title":"In Computing with Spatial Trajectories"},{"key":"10.3233\/JIFS-230518_ref2","unstructured":"Muhammad Z. , 17 percent of people admit using social media location data to try and run not someone, Digital Information World (2020). https:\/\/www.digitalinformationworld.com\/\/06\/lurking-on-locations-exploring-how-people-use-location-services-and-social-media-check-ins.html."},{"issue":"4","key":"10.3233\/JIFS-230518_ref3","doi-asserted-by":"crossref","first-page":"568","DOI":"10.1080\/00330124.2014.907699","article-title":"Where in the world are you? 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