{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/93043"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/93043","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Leveraging multi-dimensional, multi-source knowledge for user preference modeling and event summarization in social media","abstract":"This Dissertation was approved for publication on 2016-07-08 at 10:07.","abstract_html":"This Dissertation was approved for publication on 2016-07-08 at 10:07.","abstract_has_math":false,"creators":["Wang, Jingjing"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Han, Jiawei","Zhai, ChengXiang","Hockenmaier, Julia","Mei, Qiaozhu"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2016,"date_issued":"2016-11-10T18:42:51Z","date_published":"2016-11-10T18:42:51Z","updated_at":"2026-07-22T22:26:35Z","subjects":["user preference","event summarization"],"languages":["en"],"rights":["Copyright 2016 Jingjing Wang"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/93043","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Han, Jiawei","Zhai, ChengXiang","Hockenmaier, Julia","Mei, Qiaozhu"]},{"key":"dc:creator","label":"Author","values":["Wang, Jingjing"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2016-11-10T18:42:51Z","2018-11-11T10:15:11Z","2016-07-08","2016-08"]},{"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":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"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":["user preference","event summarization"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2016 Jingjing Wang"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/93043"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["This Dissertation was approved for publication on 2016-07-08 at 10:07.","DSpace SAF Submission Ingestion Package generated from Vireo submission #9812 on 2016-11-10 at 12:25:02","Made available in DSpace on 2016-11-10T18:42:51Z (GMT). No. of bitstreams: 3 WANG-DISSERTATION-2016.pdf: 2984931 bytes, checksum: a0b51ae3504c6bbd1bda908c667b0cc6 (MD5) LICENSE.txt: 4210 bytes, checksum: 66d0695c7c5dae0e1bfe354210038f98 (MD5) PROQUEST_LICENSE.txt: 4556 bytes, checksum: b6156e56d6dac4758fa898e92af88ec6 (MD5) Previous issue date: 2016-07-08","An unprecedented development of various kinds of social media platforms, such as Twitter, Facebook and Foursquare, has been witnessed in recent years. This huge amount of user generated data are multi-dimensional in nature. Some dimensions are explicitly observed such as user profiles, text of social media posts, time, and location information. Others can be implicit and need to be inferred, reflecting the inherent structures of social media data. Examples include popular topics discussed in Twitter or Facebook, or the geographical clusters based on user check-in activities from Foursquare. It is of great interest to both research communities and commercial organizations to understand such heterogeneous data and leverage available information from multiple dimensions to facilitate social media applications, such as user preference modeling and event summarization. This dissertation first presents a general discriminative learning approach for modeling multi-dimensional knowledge in a supervised setting. A learning protocol is established to model both explicit and implicit knowledge in a unified manner, which applies to general classification/prediction tasks. This approach accommodates heterogeneous data dimensions with a significant boosted expressiveness of existing discriminative learning approaches. It stands out with its capability to model latent features, for which arbitrary generative assumptions are allowed. Besides the multi-dimensional nature, social media data are unstructured, fragmented and noisy. It makes social media data mining even more challenging that a lot of real applications come with no available annotation in an unsupervised setting. This dissertation addresses this issue from a novel angle: external sources such as news media and knowledge bases are exploited to provide supervision. I describe a unified framework which links traditional news data to Twitter and enables effective knowledge discovery such as event detection and summarization.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2018-08-01","The student, Jingjing Wang, accepted the attached license on 2016-07-07 at 19:51.","The student, Jingjing Wang, submitted this Dissertation for approval on 2016-07-07 at 19:52.","Embargo set by: Seth Robbins for item 95466 Lift date: 2018-11-10T18:43:22Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","U of I Only Restriction Lifted for Item 95466 on 2018-11-11T10:15:11Z."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Leveraging multi-dimensional, multi-source knowledge for user preference modeling and event summarization in social media"]}]}],"canonical_facts":{"dc:contributor":["Han, Jiawei","Zhai, ChengXiang","Hockenmaier, Julia","Mei, Qiaozhu"],"dc:creator":["Wang, Jingjing"],"dc:date":["2016-11-10T18:42:51Z","2018-11-11T10:15:11Z","2016-07-08","2016-08"],"dc:description":["This Dissertation was approved for publication on 2016-07-08 at 10:07.","DSpace SAF Submission Ingestion Package generated from Vireo submission #9812 on 2016-11-10 at 12:25:02","Made available in DSpace on 2016-11-10T18:42:51Z (GMT). No. of bitstreams: 3 WANG-DISSERTATION-2016.pdf: 2984931 bytes, checksum: a0b51ae3504c6bbd1bda908c667b0cc6 (MD5) LICENSE.txt: 4210 bytes, checksum: 66d0695c7c5dae0e1bfe354210038f98 (MD5) PROQUEST_LICENSE.txt: 4556 bytes, checksum: b6156e56d6dac4758fa898e92af88ec6 (MD5) Previous issue date: 2016-07-08","An unprecedented development of various kinds of social media platforms, such as Twitter, Facebook and Foursquare, has been witnessed in recent years. This huge amount of user generated data are multi-dimensional in nature. Some dimensions are explicitly observed such as user profiles, text of social media posts, time, and location information. Others can be implicit and need to be inferred, reflecting the inherent structures of social media data. Examples include popular topics discussed in Twitter or Facebook, or the geographical clusters based on user check-in activities from Foursquare. It is of great interest to both research communities and commercial organizations to understand such heterogeneous data and leverage available information from multiple dimensions to facilitate social media applications, such as user preference modeling and event summarization. This dissertation first presents a general discriminative learning approach for modeling multi-dimensional knowledge in a supervised setting. A learning protocol is established to model both explicit and implicit knowledge in a unified manner, which applies to general classification/prediction tasks. This approach accommodates heterogeneous data dimensions with a significant boosted expressiveness of existing discriminative learning approaches. It stands out with its capability to model latent features, for which arbitrary generative assumptions are allowed. Besides the multi-dimensional nature, social media data are unstructured, fragmented and noisy. It makes social media data mining even more challenging that a lot of real applications come with no available annotation in an unsupervised setting. This dissertation addresses this issue from a novel angle: external sources such as news media and knowledge bases are exploited to provide supervision. I describe a unified framework which links traditional news data to Twitter and enables effective knowledge discovery such as event detection and summarization.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2018-08-01","The student, Jingjing Wang, accepted the attached license on 2016-07-07 at 19:51.","The student, Jingjing Wang, submitted this Dissertation for approval on 2016-07-07 at 19:52.","Embargo set by: Seth Robbins for item 95466 Lift date: 2018-11-10T18:43:22Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","U of I Only Restriction Lifted for Item 95466 on 2018-11-11T10:15:11Z."],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/2142/93043"],"dc:language":["en"],"dc:rights":["Copyright 2016 Jingjing Wang"],"dc:subject":["user preference","event summarization"],"dc:title":["Leveraging multi-dimensional, multi-source knowledge for user preference modeling and event summarization in social media"],"dc:type":["text"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:26:35Z"}