{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/78673"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/78673","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"EKNOT: Event Knowledge from News and Opinions on Twitter","abstract":"We present the EKNOT system that automatically discovers major events from online news articles, connects each event to its discussion on Twitter, and provides a comprehensive summary of the events from both news media and social media’s point of view. EKNOT takes a time period as input and outputs a complete picture of what happened and the public’s opinions. For each event, EKNOT provides multi-dimensional summaries: a) a summary from news for an objective description; b) a summary from tweets containing opinions/sentiments; c) an en- tity graph which illustrates the major players involved and their correlations; d) the time span of the event; and e) an opinion (sentiment) distribution. A user-friendly interface is provided to facilitate interactive exploration of the mining results: if a user is interested in a particular event, he/she can zoom in this event to investigate its multiple aspects (sub-events). These aspects will be summarized in the same way with the above features. Furthermore, EKNOT is built on real-time crawled news articles and tweets. The efficient data collection and processing scheme allows users to explore the dynamics of major events from the perspectives of both news and social media in near real-time.","abstract_html":"We present the EKNOT system that automatically discovers major events from online news articles, connects each event to its discussion on Twitter, and provides a comprehensive summary of the events from both news media and social media’s point of view. EKNOT takes a time period as input and outputs a complete picture of what happened and the public’s opinions. For each event, EKNOT provides multi-dimensional summaries: a) a summary from news for an objective description; b) a summary from tweets containing opinions/sentiments; c) an en- tity graph which illustrates the major players involved and their correlations; d) the time span of the event; and e) an opinion (sentiment) distribution. A user-friendly interface is provided to facilitate interactive exploration of the mining results: if a user is interested in a particular event, he/she can zoom in this event to investigate its multiple aspects (sub-events). These aspects will be summarized in the same way with the above features. Furthermore, EKNOT is built on real-time crawled news articles and tweets. The efficient data collection and processing scheme allows users to explore the dynamics of major events from the perspectives of both news and social media in near real-time.","abstract_has_math":false,"creators":["Li, Min"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2015,"date_issued":"2015-07-22T22:33:55Z","date_published":"2015-07-22T22:33:55Z","updated_at":"2026-07-22T22:26:12Z","subjects":["system","event discovery"],"languages":["en"],"rights":["Copyright 2015 Min Li"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/78673","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Li, Min"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2015-07-22T22:33:55Z","2017-07-23T09:15:17Z","2015-05","2015-04-26","2015-5"]},{"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":["system","event 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 2015 Min Li"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/78673"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["We present the EKNOT system that automatically discovers major events from online news articles, connects each event to its discussion on Twitter, and provides a comprehensive summary of the events from both news media and social media’s point of view. EKNOT takes a time period as input and outputs a complete picture of what happened and the public’s opinions. For each event, EKNOT provides multi-dimensional summaries: a) a summary from news for an objective description; b) a summary from tweets containing opinions/sentiments; c) an en- tity graph which illustrates the major players involved and their correlations; d) the time span of the event; and e) an opinion (sentiment) distribution. A user-friendly interface is provided to facilitate interactive exploration of the mining results: if a user is interested in a particular event, he/she can zoom in this event to investigate its multiple aspects (sub-events). These aspects will be summarized in the same way with the above features. Furthermore, EKNOT is built on real-time crawled news articles and tweets. The efficient data collection and processing scheme allows users to explore the dynamics of major events from the perspectives of both news and social media in near real-time.","Submission published under a 24 month embargo labeled 'U of I only', the embargo will last until 2017-05-01","The student, Min Li, accepted the attached license on 2015-04-24 at 15:43.","The student, Min Li, submitted this Thesis for approval on 2015-04-24 at 15:59.","This Thesis was approved for publication on 2015-04-26 at 11:17.","DSpace SAF Submission Ingestion Package generated from Vireo submission #8099 on 2015-07-22 at 14:18:53","Made available in DSpace on 2015-07-22T22:33:55Z (GMT). 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EKNOT takes a time period as input and outputs a complete picture of what happened and the public’s opinions. For each event, EKNOT provides multi-dimensional summaries: a) a summary from news for an objective description; b) a summary from tweets containing opinions/sentiments; c) an en- tity graph which illustrates the major players involved and their correlations; d) the time span of the event; and e) an opinion (sentiment) distribution. A user-friendly interface is provided to facilitate interactive exploration of the mining results: if a user is interested in a particular event, he/she can zoom in this event to investigate its multiple aspects (sub-events). These aspects will be summarized in the same way with the above features. Furthermore, EKNOT is built on real-time crawled news articles and tweets. 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