{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/90658"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/90658","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Mapping the world with social buzz","abstract":"With the advancement of information technology, social networks have become popular platforms for people to share and discuss their activities and interests. The social network contents, the social buzz in these networks, are a valuable source for us to learn about the world. Meanwhile, the development of geotagging technology allows us to link such contents on these virtual networks with real-world locations. Taking advantage of the popularity of social networks and the geotagging technology, in this project, we desire to map the world with social buzz. In other words, we aim at building a map system enriched by social network contents, which is beneficial to both individuals and businesses, as the area profiles summarized from social buzz can help them to understand the areas conveniently. We build our system in three steps: collecting contents, training the model and presenting the model. The core of our system is a tree-structure model that summarizes social network contents for areas in different levels. In this model, we profile each area's popularity, topic and geographic information. With this model, we develop a generative process to describe how a document is composed. An estimation inference algorithm based on Gibbs sampling is provided to learn an optimal tree model from a set of observed documents. Evaluation results show that our system is effective.","abstract_html":"With the advancement of information technology, social networks have become popular platforms for people to share and discuss their activities and interests. The social network contents, the social buzz in these networks, are a valuable source for us to learn about the world. Meanwhile, the development of geotagging technology allows us to link such contents on these virtual networks with real-world locations. Taking advantage of the popularity of social networks and the geotagging technology, in this project, we desire to map the world with social buzz. In other words, we aim at building a map system enriched by social network contents, which is beneficial to both individuals and businesses, as the area profiles summarized from social buzz can help them to understand the areas conveniently. We build our system in three steps: collecting contents, training the model and presenting the model. The core of our system is a tree-structure model that summarizes social network contents for areas in different levels. In this model, we profile each area's popularity, topic and geographic information. With this model, we develop a generative process to describe how a document is composed. An estimation inference algorithm based on Gibbs sampling is provided to learn an optimal tree model from a set of observed documents. Evaluation results show that our system is effective.","abstract_has_math":false,"creators":["Zhang, Zhijun"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Chang, Kevin C.C."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2016,"date_issued":"2016-07-07T19:58:07Z","date_published":"2016-07-07T19:58:07Z","updated_at":"2026-07-22T22:26:34Z","subjects":["social networks","geographic topics","topic modeling"],"languages":["en"],"rights":["Copyright 2016 Zhijun Zhang"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/90658","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Chang, Kevin C.C."]},{"key":"dc:creator","label":"Author","values":["Zhang, Zhijun"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2016-07-07T19:58:07Z","2016-04-26","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":["social networks","geographic topics","topic modeling"]}]},{"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 Zhijun Zhang"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/90658"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["With the advancement of information technology, social networks have become popular platforms for people to share and discuss their activities and interests. The social network contents, the social buzz in these networks, are a valuable source for us to learn about the world. Meanwhile, the development of geotagging technology allows us to link such contents on these virtual networks with real-world locations. Taking advantage of the popularity of social networks and the geotagging technology, in this project, we desire to map the world with social buzz. In other words, we aim at building a map system enriched by social network contents, which is beneficial to both individuals and businesses, as the area profiles summarized from social buzz can help them to understand the areas conveniently. We build our system in three steps: collecting contents, training the model and presenting the model. The core of our system is a tree-structure model that summarizes social network contents for areas in different levels. In this model, we profile each area's popularity, topic and geographic information. With this model, we develop a generative process to describe how a document is composed. An estimation inference algorithm based on Gibbs sampling is provided to learn an optimal tree model from a set of observed documents. Evaluation results show that our system is effective.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2016-07-07 without embargo terms","The student, Zhijun Zhang, accepted the attached license on 2016-04-26 at 13:03.","The student, Zhijun Zhang, submitted this Thesis for approval on 2016-04-26 at 13:14.","This Thesis was approved for publication on 2016-04-26 at 14:44.","DSpace SAF Submission Ingestion Package generated from Vireo submission #9511 on 2016-07-07 at 13:33:23","Made available in DSpace on 2016-07-07T19:58:07Z (GMT). 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Taking advantage of the popularity of social networks and the geotagging technology, in this project, we desire to map the world with social buzz. In other words, we aim at building a map system enriched by social network contents, which is beneficial to both individuals and businesses, as the area profiles summarized from social buzz can help them to understand the areas conveniently. We build our system in three steps: collecting contents, training the model and presenting the model. The core of our system is a tree-structure model that summarizes social network contents for areas in different levels. In this model, we profile each area's popularity, topic and geographic information. With this model, we develop a generative process to describe how a document is composed. An estimation inference algorithm based on Gibbs sampling is provided to learn an optimal tree model from a set of observed documents. Evaluation results show that our system is effective.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2016-07-07 without embargo terms","The student, Zhijun Zhang, accepted the attached license on 2016-04-26 at 13:03.","The student, Zhijun Zhang, submitted this Thesis for approval on 2016-04-26 at 13:14.","This Thesis was approved for publication on 2016-04-26 at 14:44.","DSpace SAF Submission Ingestion Package generated from Vireo submission #9511 on 2016-07-07 at 13:33:23","Made available in DSpace on 2016-07-07T19:58:07Z (GMT). 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