{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/101382"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/101382","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Tweets2Cube: Interactive spatiotemporal knowledge acquisition from massive social media","abstract":"We present the Tweets2Cube system that uncovers the patterns underlying people's spatiotemporal activities from massive online social media. Tweets2Cube organizes unstructured social media records into a multi-dimensional data cube along three dimensions: (1) what is the user's activity; (2) where does that activity occur; and (3) when does that activity occur. As such, the end users can use simple queries to retrieve task-relevant sub-corpus from the data cube in a flexible way. Moreover, Tweets2Cube consists of a set of spatiotemporal modeling algorithms, which can be readily applied to the retrieved data for extracting knowledge about people's activities in the physical world. Such algorithms jointly model location, time, and text and are capable of discovering a variety of patterns, such as routine spatiotemporal activities, unusual events, and mobility patterns. With Tweets2Cube, the end users can interactively retrieve task-relevant social media and choose appropriate spatiotemporal modeling algorithms for knowledge acquisition, which makes Tweets2Cube highly useful for downstream tasks like disaster relief, targeted advertising, and location-based recommendation.","abstract_html":"We present the Tweets2Cube system that uncovers the patterns underlying people's spatiotemporal activities from massive online social media. Tweets2Cube organizes unstructured social media records into a multi-dimensional data cube along three dimensions: (1) what is the user's activity; (2) where does that activity occur; and (3) when does that activity occur. As such, the end users can use simple queries to retrieve task-relevant sub-corpus from the data cube in a flexible way. Moreover, Tweets2Cube consists of a set of spatiotemporal modeling algorithms, which can be readily applied to the retrieved data for extracting knowledge about people's activities in the physical world. Such algorithms jointly model location, time, and text and are capable of discovering a variety of patterns, such as routine spatiotemporal activities, unusual events, and mobility patterns. With Tweets2Cube, the end users can interactively retrieve task-relevant social media and choose appropriate spatiotemporal modeling algorithms for knowledge acquisition, which makes Tweets2Cube highly useful for downstream tasks like disaster relief, targeted advertising, and location-based recommendation.","abstract_has_math":false,"creators":["Li, Lunan"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Han, Jiawei"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-09-04T20:47:32Z","date_published":"2018-09-04T20:47:32Z","updated_at":"2026-07-22T22:24:40Z","subjects":["Social Media, Social Cube"],"languages":["en"],"rights":["Copyright 2018 Lunan Li"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/101382","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Han, Jiawei"]},{"key":"dc:creator","label":"Author","values":["Li, Lunan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2018-09-04T20:47:32Z","2020-09-05T09:15:17Z","2018-04-26","2018-05"]},{"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":["Social Media, Social Cube"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2018 Lunan Li"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/101382"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["We present the Tweets2Cube system that uncovers the patterns underlying people's spatiotemporal activities from massive online social media. Tweets2Cube organizes unstructured social media records into a multi-dimensional data cube along three dimensions: (1) what is the user's activity; (2) where does that activity occur; and (3) when does that activity occur. As such, the end users can use simple queries to retrieve task-relevant sub-corpus from the data cube in a flexible way. Moreover, Tweets2Cube consists of a set of spatiotemporal modeling algorithms, which can be readily applied to the retrieved data for extracting knowledge about people's activities in the physical world. Such algorithms jointly model location, time, and text and are capable of discovering a variety of patterns, such as routine spatiotemporal activities, unusual events, and mobility patterns. With Tweets2Cube, the end users can interactively retrieve task-relevant social media and choose appropriate spatiotemporal modeling algorithms for knowledge acquisition, which makes Tweets2Cube highly useful for downstream tasks like disaster relief, targeted advertising, and location-based recommendation.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2020-05-01","The student, Lunan Li, accepted the attached license on 2018-04-26 at 10:51.","The student, Lunan Li, submitted this Thesis for approval on 2018-04-26 at 10:57.","This Thesis was approved for publication on 2018-04-26 at 12:28.","DSpace SAF Submission Ingestion Package generated from Vireo submission #12520 on 2018-08-31 at 17:30:34","Made available in DSpace on 2018-09-04T20:47:32Z (GMT). No. of bitstreams: 2 LI-THESIS-2018.pdf: 5283176 bytes, checksum: b79eed06edf1a01895b621dd370f53af (MD5) LICENSE.txt: 4205 bytes, checksum: 08b3977db855ae2712cefb3fdad2f8a4 (MD5) Previous issue date: 2018-04-26","Embargo set by: Seth Robbins for item 107467 Lift date: 2020-09-04T20:47:38Z Reason: Author requested closed access (OA after 2yrs) in Vireo ETD system","Embargo set by: Seth Robbins for item 107467 Lift date: 2020-09-04T20:50:11Z Reason: Author requested closed access (OA after 2yrs) in Vireo ETD system","Limited Restriction Lifted for Item 107467 on 2020-09-05T09:15:17Z."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Tweets2Cube: Interactive spatiotemporal knowledge acquisition from massive social media"]}]}],"canonical_facts":{"dc:contributor":["Han, Jiawei"],"dc:creator":["Li, Lunan"],"dc:date":["2018-09-04T20:47:32Z","2020-09-05T09:15:17Z","2018-04-26","2018-05"],"dc:description":["We present the Tweets2Cube system that uncovers the patterns underlying people's spatiotemporal activities from massive online social media. Tweets2Cube organizes unstructured social media records into a multi-dimensional data cube along three dimensions: (1) what is the user's activity; (2) where does that activity occur; and (3) when does that activity occur. As such, the end users can use simple queries to retrieve task-relevant sub-corpus from the data cube in a flexible way. Moreover, Tweets2Cube consists of a set of spatiotemporal modeling algorithms, which can be readily applied to the retrieved data for extracting knowledge about people's activities in the physical world. Such algorithms jointly model location, time, and text and are capable of discovering a variety of patterns, such as routine spatiotemporal activities, unusual events, and mobility patterns. With Tweets2Cube, the end users can interactively retrieve task-relevant social media and choose appropriate spatiotemporal modeling algorithms for knowledge acquisition, which makes Tweets2Cube highly useful for downstream tasks like disaster relief, targeted advertising, and location-based recommendation.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2020-05-01","The student, Lunan Li, accepted the attached license on 2018-04-26 at 10:51.","The student, Lunan Li, submitted this Thesis for approval on 2018-04-26 at 10:57.","This Thesis was approved for publication on 2018-04-26 at 12:28.","DSpace SAF Submission Ingestion Package generated from Vireo submission #12520 on 2018-08-31 at 17:30:34","Made available in DSpace on 2018-09-04T20:47:32Z (GMT). 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