{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/97270"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/97270","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Exploitation of information propagation patterns in social sensing","abstract":"Online social media presents new opportunity for sensing the physical world. The sensors are essentially human, who share information in the broadcast social media. Such human sensors impose challenges like influence, bias, polarization, and data overload, unseen in the traditional sensor network. This dissertation addresses the aforementioned challenges by exploiting the propagation or prefential attachment patterns of the human sensors to distill a factual view of the events transpiring in the physical world. Our first contribution explores the correlated errors caused by the dependent sources. When people follow others, they are prone to broadcast information with unknown provenance. We show that using admission control mechanism to select an independent set of sensors improves the quality of reconstruction. The next contribution explores a different kind of correlated error caused by polarization and bias. During events related to conflict or disagreement, people take sides, and take a selective or preferential approach when broadcasting information. For example, a source might be less credible when it shares information conforming to its own bias. We present a maximum-likelihood estimation model to reconstruct the factual information in such cases, given the individual bias of the sources are already known. Our next two contributions relate to modeling polarization and unveiling polarization using maximum-likelihood and matrix factorization based mechanisms. These mechanisms allow us to automate the process of separating polarized content, and obtain a more faithful view of the events being sensed. Finally, we design and implement `SocialTrove', a summarization service that continuously execute in the cloud, as a platform to compute the reconstructions at scale. Our contributions have been integrated with `Apollo Social Sensing Toolkit', which builds a pipeline to collect, summarize, and analyze information from Twitter, and serves more than 40 users.","abstract_html":"Online social media presents new opportunity for sensing the physical world. The sensors are essentially human, who share information in the broadcast social media. Such human sensors impose challenges like influence, bias, polarization, and data overload, unseen in the traditional sensor network. This dissertation addresses the aforementioned challenges by exploiting the propagation or prefential attachment patterns of the human sensors to distill a factual view of the events transpiring in the physical world. Our first contribution explores the correlated errors caused by the dependent sources. When people follow others, they are prone to broadcast information with unknown provenance. We show that using admission control mechanism to select an independent set of sensors improves the quality of reconstruction. The next contribution explores a different kind of correlated error caused by polarization and bias. During events related to conflict or disagreement, people take sides, and take a selective or preferential approach when broadcasting information. For example, a source might be less credible when it shares information conforming to its own bias. We present a maximum-likelihood estimation model to reconstruct the factual information in such cases, given the individual bias of the sources are already known. Our next two contributions relate to modeling polarization and unveiling polarization using maximum-likelihood and matrix factorization based mechanisms. These mechanisms allow us to automate the process of separating polarized content, and obtain a more faithful view of the events being sensed. Finally, we design and implement `SocialTrove', a summarization service that continuously execute in the cloud, as a platform to compute the reconstructions at scale. Our contributions have been integrated with `Apollo Social Sensing Toolkit', which builds a pipeline to collect, summarize, and analyze information from Twitter, and serves more than 40 users.","abstract_has_math":false,"creators":["Amin, Md Tanvir Al"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Abdelzaher, Tarek F.","Gupta, Indranil","Parameswaran, Aditya","Srivatsa, Mudhakar"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2017,"date_issued":"2017-08-10T19:14:32Z","date_published":"2017-08-10T19:14:32Z","updated_at":"2026-07-22T22:24:32Z","subjects":["Social sensing","Summarization service","Fact-finder","Social dependency","Polarization","Information propagation patterns","Correlated error","Expectation maximization","Maximum likelihood","Matrix factorization","Hierarchical clustering","Tweet clustering"],"languages":["en"],"rights":["Copyright 2017 Md Tanvir Al Amin"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/97270","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Abdelzaher, Tarek F.","Gupta, Indranil","Parameswaran, Aditya","Srivatsa, Mudhakar"]},{"key":"dc:creator","label":"Author","values":["Amin, Md Tanvir Al"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2017-08-10T19:14:32Z","2017-03-13","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":["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":["Social sensing","Summarization service","Fact-finder","Social dependency","Polarization","Information propagation patterns","Correlated error","Expectation maximization","Maximum likelihood","Matrix factorization","Hierarchical clustering","Tweet clustering"]}]},{"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 Md Tanvir Al Amin"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/97270"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Online social media presents new opportunity for sensing the physical world. The sensors are essentially human, who share information in the broadcast social media. Such human sensors impose challenges like influence, bias, polarization, and data overload, unseen in the traditional sensor network. This dissertation addresses the aforementioned challenges by exploiting the propagation or prefential attachment patterns of the human sensors to distill a factual view of the events transpiring in the physical world. Our first contribution explores the correlated errors caused by the dependent sources. When people follow others, they are prone to broadcast information with unknown provenance. We show that using admission control mechanism to select an independent set of sensors improves the quality of reconstruction. The next contribution explores a different kind of correlated error caused by polarization and bias. During events related to conflict or disagreement, people take sides, and take a selective or preferential approach when broadcasting information. For example, a source might be less credible when it shares information conforming to its own bias. We present a maximum-likelihood estimation model to reconstruct the factual information in such cases, given the individual bias of the sources are already known. Our next two contributions relate to modeling polarization and unveiling polarization using maximum-likelihood and matrix factorization based mechanisms. These mechanisms allow us to automate the process of separating polarized content, and obtain a more faithful view of the events being sensed. Finally, we design and implement `SocialTrove', a summarization service that continuously execute in the cloud, as a platform to compute the reconstructions at scale. Our contributions have been integrated with `Apollo Social Sensing Toolkit', which builds a pipeline to collect, summarize, and analyze information from Twitter, and serves more than 40 users.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2017-08-10 without embargo terms","The student, Md Tanvir Amin, accepted the attached license on 2017-03-10 at 12:34.","The student, Md Tanvir Amin, submitted this Dissertation for approval on 2017-03-10 at 12:36.","This Dissertation was approved for publication on 2017-03-13 at 08:50.","DSpace SAF Submission Ingestion Package generated from Vireo submission #10585 on 2017-08-10 at 13:38:03","Made available in DSpace on 2017-08-10T19:14:32Z (GMT). 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This dissertation addresses the aforementioned challenges by exploiting the propagation or prefential attachment patterns of the human sensors to distill a factual view of the events transpiring in the physical world. Our first contribution explores the correlated errors caused by the dependent sources. When people follow others, they are prone to broadcast information with unknown provenance. We show that using admission control mechanism to select an independent set of sensors improves the quality of reconstruction. The next contribution explores a different kind of correlated error caused by polarization and bias. During events related to conflict or disagreement, people take sides, and take a selective or preferential approach when broadcasting information. For example, a source might be less credible when it shares information conforming to its own bias. We present a maximum-likelihood estimation model to reconstruct the factual information in such cases, given the individual bias of the sources are already known. Our next two contributions relate to modeling polarization and unveiling polarization using maximum-likelihood and matrix factorization based mechanisms. These mechanisms allow us to automate the process of separating polarized content, and obtain a more faithful view of the events being sensed. Finally, we design and implement `SocialTrove', a summarization service that continuously execute in the cloud, as a platform to compute the reconstructions at scale. Our contributions have been integrated with `Apollo Social Sensing Toolkit', which builds a pipeline to collect, summarize, and analyze information from Twitter, and serves more than 40 users.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2017-08-10 without embargo terms","The student, Md Tanvir Amin, accepted the attached license on 2017-03-10 at 12:34.","The student, Md Tanvir Amin, submitted this Dissertation for approval on 2017-03-10 at 12:36.","This Dissertation was approved for publication on 2017-03-13 at 08:50.","DSpace SAF Submission Ingestion Package generated from Vireo submission #10585 on 2017-08-10 at 13:38:03","Made available in DSpace on 2017-08-10T19:14:32Z (GMT). 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