{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/90794"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/90794","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Modeling trustworthy opinion using an uncertainty-aware approach","abstract":"In this era of information explosion, conflicts are often encountered when information is provided by multiple sources. Traditional truth discovery task aims to identify the truth – the most trustworthy information, from conflicting sources in different scenarios. In this kind of tasks, truth is regarded as a fixed value or a set of fixed values. However, in a number of real-world cases, objective truth existence cannot be ensured and we can only identify single or multiple reliable facts from opinions. Different from traditional truth discovery task, we address this uncertainty and introduce the concept of trustworthy opinion of an entity, treat it as a random variable, and use its distribution to describe consistency or controversy, which is particularly difficult for data which can be numerically or categorically measured. In this study, we propose a Trustworthy Opinion Model (TOM) to model its controversy and consistency, which focusing on both quantitative and categorical opinion. The model uses a Kernel Density Estimation based uncertainty-aware approach to estimate its probability distribution, and summarize trustworthy information based on this distribution. Experiments indicate that TOM not only has outstanding performance on the classical numeric truth discovery task, but also shows good performance on multi-modality detection and anomaly detection in the uncertain-opinion setting.","abstract_html":"In this era of information explosion, conflicts are often encountered when information is provided by multiple sources. Traditional truth discovery task aims to identify the truth – the most trustworthy information, from conflicting sources in different scenarios. In this kind of tasks, truth is regarded as a fixed value or a set of fixed values. However, in a number of real-world cases, objective truth existence cannot be ensured and we can only identify single or multiple reliable facts from opinions. Different from traditional truth discovery task, we address this uncertainty and introduce the concept of trustworthy opinion of an entity, treat it as a random variable, and use its distribution to describe consistency or controversy, which is particularly difficult for data which can be numerically or categorically measured. In this study, we propose a Trustworthy Opinion Model (TOM) to model its controversy and consistency, which focusing on both quantitative and categorical opinion. The model uses a Kernel Density Estimation based uncertainty-aware approach to estimate its probability distribution, and summarize trustworthy information based on this distribution. Experiments indicate that TOM not only has outstanding performance on the classical numeric truth discovery task, but also shows good performance on multi-modality detection and anomaly detection in the uncertain-opinion setting.","abstract_has_math":false,"creators":["Chen, Xiangyu"],"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","Rosenbaum, Elyse"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2016,"date_issued":"2016-07-07T20:27:45Z","date_published":"2016-07-07T20:27:45Z","updated_at":"2026-07-22T22:26:34Z","subjects":["trustworthy opinion","truth discovery"],"languages":["en"],"rights":["Copyright 2016 Xiangyu Chen"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/90794","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Han, Jiawei","Rosenbaum, Elyse"]},{"key":"dc:creator","label":"Author","values":["Chen, Xiangyu"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2016-07-07T20:27:45Z","2018-07-08T09:15:16Z","2016-04-20","2016-05"]},{"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":["trustworthy opinion","truth discovery"]}]},{"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 Xiangyu Chen"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/90794"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["In this era of information explosion, conflicts are often encountered when information is provided by multiple sources. Traditional truth discovery task aims to identify the truth – the most trustworthy information, from conflicting sources in different scenarios. In this kind of tasks, truth is regarded as a fixed value or a set of fixed values. However, in a number of real-world cases, objective truth existence cannot be ensured and we can only identify single or multiple reliable facts from opinions. Different from traditional truth discovery task, we address this uncertainty and introduce the concept of trustworthy opinion of an entity, treat it as a random variable, and use its distribution to describe consistency or controversy, which is particularly difficult for data which can be numerically or categorically measured. In this study, we propose a Trustworthy Opinion Model (TOM) to model its controversy and consistency, which focusing on both quantitative and categorical opinion. The model uses a Kernel Density Estimation based uncertainty-aware approach to estimate its probability distribution, and summarize trustworthy information based on this distribution. Experiments indicate that TOM not only has outstanding performance on the classical numeric truth discovery task, but also shows good performance on multi-modality detection and anomaly detection in the uncertain-opinion setting.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2018-05-01","The student, Xiangyu Chen, accepted the attached license on 2016-04-19 at 11:08.","The student, Xiangyu Chen, submitted this Thesis for approval on 2016-04-19 at 11:16.","This Thesis was approved for publication on 2016-04-20 at 10:14.","DSpace SAF Submission Ingestion Package generated from Vireo submission #9334 on 2016-07-07 at 13:50:06","Made available in DSpace on 2016-07-07T20:27:45Z (GMT). No. of bitstreams: 2 CHEN-THESIS-2016.pdf: 801853 bytes, checksum: f9a1efaaa27b32eaa61313f5d1aa965d (MD5) LICENSE.txt: 4209 bytes, checksum: cbfad139db2966383f1a4929f0e6bcf4 (MD5) Previous issue date: 2016-04-20","Embargo set by: Seth Robbins for item 93146 Lift date: 2018-07-07T20:28:14Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","Embargo set by: Seth Robbins for item 93146 Lift date: 2018-07-07T20:35:34Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","U of I Only Restriction Lifted for Item 93146 on 2018-07-08T09:15:16Z."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Modeling trustworthy opinion using an uncertainty-aware approach"]}]}],"canonical_facts":{"dc:contributor":["Han, Jiawei","Rosenbaum, Elyse"],"dc:creator":["Chen, Xiangyu"],"dc:date":["2016-07-07T20:27:45Z","2018-07-08T09:15:16Z","2016-04-20","2016-05"],"dc:description":["In this era of information explosion, conflicts are often encountered when information is provided by multiple sources. Traditional truth discovery task aims to identify the truth – the most trustworthy information, from conflicting sources in different scenarios. In this kind of tasks, truth is regarded as a fixed value or a set of fixed values. However, in a number of real-world cases, objective truth existence cannot be ensured and we can only identify single or multiple reliable facts from opinions. Different from traditional truth discovery task, we address this uncertainty and introduce the concept of trustworthy opinion of an entity, treat it as a random variable, and use its distribution to describe consistency or controversy, which is particularly difficult for data which can be numerically or categorically measured. In this study, we propose a Trustworthy Opinion Model (TOM) to model its controversy and consistency, which focusing on both quantitative and categorical opinion. The model uses a Kernel Density Estimation based uncertainty-aware approach to estimate its probability distribution, and summarize trustworthy information based on this distribution. Experiments indicate that TOM not only has outstanding performance on the classical numeric truth discovery task, but also shows good performance on multi-modality detection and anomaly detection in the uncertain-opinion setting.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2018-05-01","The student, Xiangyu Chen, accepted the attached license on 2016-04-19 at 11:08.","The student, Xiangyu Chen, submitted this Thesis for approval on 2016-04-19 at 11:16.","This Thesis was approved for publication on 2016-04-20 at 10:14.","DSpace SAF Submission Ingestion Package generated from Vireo submission #9334 on 2016-07-07 at 13:50:06","Made available in DSpace on 2016-07-07T20:27:45Z (GMT). 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