{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/97632"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/97632","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Neural geolocation prediction in Twitter","abstract":"Inferring the location of a user has been a valuable step for many applications that leverage social media, such as marketing, security monitoring and recommendation systems. Motivated by the recent success of Deep Learning techniques for many tasks such as computer vision, speech recognition, and natural language processing, we study the application of neural models to the problem of geolocation prediction and experiment with multiple techniques to analyze neural networks for geolocation inference based solely on text. Experimental results on the dataset suggest that choosing appropriate network architecture can all increase performance on this task and demonstrate a promising extension of neural network based models for geolocation prediction. Our systematic extensive study of four supervised and three unsupervised tweet representations reveal that Convolutional Neural Networks (CNNs) and fastText best encode the the textual and geoloca- tional properties of tweets respectively. fastText emerges as the best model for low resource settings, providing very little degradation with reduction in embedding size.","abstract_html":"Inferring the location of a user has been a valuable step for many applications that leverage social media, such as marketing, security monitoring and recommendation systems. Motivated by the recent success of Deep Learning techniques for many tasks such as computer vision, speech recognition, and natural language processing, we study the application of neural models to the problem of geolocation prediction and experiment with multiple techniques to analyze neural networks for geolocation inference based solely on text. Experimental results on the dataset suggest that choosing appropriate network architecture can all increase performance on this task and demonstrate a promising extension of neural network based models for geolocation prediction. Our systematic extensive study of four supervised and three unsupervised tweet representations reveal that Convolutional Neural Networks (CNNs) and fastText best encode the the textual and geoloca- tional properties of tweets respectively. fastText emerges as the best model for low resource settings, providing very little degradation with reduction in embedding size.","abstract_has_math":false,"creators":["Srinivasan, Pramod"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Zhai, ChengXiang"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2017,"date_issued":"2017-08-10T19:52:23Z","date_published":"2017-08-10T19:52:23Z","updated_at":"2026-07-22T22:24:34Z","subjects":["Deep learning","Information retrieval"],"languages":["en"],"rights":["Copyright 2017 Pramod Srinivasan"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/97632","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Zhai, ChengXiang"]},{"key":"dc:creator","label":"Author","values":["Srinivasan, Pramod"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2017-08-10T19:52:23Z","2019-08-11T09:15:17Z","2017-04-25","2017-05"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Deep learning","Information retrieval"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2017 Pramod Srinivasan"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/97632"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Inferring the location of a user has been a valuable step for many applications that leverage social media, such as marketing, security monitoring and recommendation systems. Motivated by the recent success of Deep Learning techniques for many tasks such as computer vision, speech recognition, and natural language processing, we study the application of neural models to the problem of geolocation prediction and experiment with multiple techniques to analyze neural networks for geolocation inference based solely on text. Experimental results on the dataset suggest that choosing appropriate network architecture can all increase performance on this task and demonstrate a promising extension of neural network based models for geolocation prediction. Our systematic extensive study of four supervised and three unsupervised tweet representations reveal that Convolutional Neural Networks (CNNs) and fastText best encode the the textual and geoloca- tional properties of tweets respectively. fastText emerges as the best model for low resource settings, providing very little degradation with reduction in embedding size.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2019-05-01","The student, Pramod Srinivasan, accepted the attached license on 2017-04-25 at 12:15.","The student, Pramod Srinivasan, submitted this Thesis for approval on 2017-04-25 at 12:51.","This Thesis was approved for publication on 2017-04-25 at 18:42.","DSpace SAF Submission Ingestion Package generated from Vireo submission #11043 on 2017-08-10 at 14:32:36","Made available in DSpace on 2017-08-10T19:52:23Z (GMT). No. of bitstreams: 2 SRINIVASAN-THESIS-2017.pdf: 1215687 bytes, checksum: 96dbc159bb19eab4d69b3df1dfcffd17 (MD5) LICENSE.txt: 4214 bytes, checksum: 6d429007259258d1f9571b8e0eac0cf7 (MD5) Previous issue date: 2017-04-25","Embargo set by: Colleen Fallaw for item 102685 Lift date: 2019-08-10T21:25:30Z Reason: Author requested closed access (OA after 2yrs) in Vireo ETD system","Limited Restriction Lifted for Item 102685 on 2019-08-11T09:15:17Z."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Neural geolocation prediction in Twitter"]}]}],"canonical_facts":{"dc:contributor":["Zhai, ChengXiang"],"dc:creator":["Srinivasan, Pramod"],"dc:date":["2017-08-10T19:52:23Z","2019-08-11T09:15:17Z","2017-04-25","2017-05"],"dc:description":["Inferring the location of a user has been a valuable step for many applications that leverage social media, such as marketing, security monitoring and recommendation systems. Motivated by the recent success of Deep Learning techniques for many tasks such as computer vision, speech recognition, and natural language processing, we study the application of neural models to the problem of geolocation prediction and experiment with multiple techniques to analyze neural networks for geolocation inference based solely on text. Experimental results on the dataset suggest that choosing appropriate network architecture can all increase performance on this task and demonstrate a promising extension of neural network based models for geolocation prediction. Our systematic extensive study of four supervised and three unsupervised tweet representations reveal that Convolutional Neural Networks (CNNs) and fastText best encode the the textual and geoloca- tional properties of tweets respectively. fastText emerges as the best model for low resource settings, providing very little degradation with reduction in embedding size.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2019-05-01","The student, Pramod Srinivasan, accepted the attached license on 2017-04-25 at 12:15.","The student, Pramod Srinivasan, submitted this Thesis for approval on 2017-04-25 at 12:51.","This Thesis was approved for publication on 2017-04-25 at 18:42.","DSpace SAF Submission Ingestion Package generated from Vireo submission #11043 on 2017-08-10 at 14:32:36","Made available in DSpace on 2017-08-10T19:52:23Z (GMT). No. of bitstreams: 2 SRINIVASAN-THESIS-2017.pdf: 1215687 bytes, checksum: 96dbc159bb19eab4d69b3df1dfcffd17 (MD5) LICENSE.txt: 4214 bytes, checksum: 6d429007259258d1f9571b8e0eac0cf7 (MD5) Previous issue date: 2017-04-25","Embargo set by: Colleen Fallaw for item 102685 Lift date: 2019-08-10T21:25:30Z Reason: Author requested closed access (OA after 2yrs) in Vireo ETD system","Limited Restriction Lifted for Item 102685 on 2019-08-11T09:15:17Z."],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/2142/97632"],"dc:language":["en"],"dc:rights":["Copyright 2017 Pramod Srinivasan"],"dc:subject":["Deep learning","Information retrieval"],"dc:title":["Neural geolocation prediction in Twitter"],"dc:type":["text"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:34Z"}