{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/50426"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/50426","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Understanding user intents in online health forums","abstract":"Online health forums provide a convenient way for patients to obtain medical information and connect with physicians and peers outside of clinical settings. However, the large quantities of unstructured and diversified content generated on these forums make it difficult for users to digest and extract useful information. Understanding the intents of people who post on these forums would enable the retrieval of relevant information from existing threads which would in turn allow users to more effectively find answers to their medical needs. In this paper, we derive a taxonomy of intents to capture user information need in online health forums, and propose novel pattern based features to classify original thread posts according to their underlying intents. Since no dataset existed for this task, we employ three annotators to manually tag a dataset of 1,200 HealthBoards posts spanning four topics. Experimentation finds that pattern based features are highly capable of identifying user intents in forum posts, reaching a precision of 75\\%. In addition, we achieve comparable classification performance by training and testing on posts from different forums, thereby showing the robustness of our method. Finally, we run our trained classifier on a MedHelp dataset to analyze the distribution of intents of different topics in the forum.","abstract_html":"Online health forums provide a convenient way for patients to obtain medical information and connect with physicians and peers outside of clinical settings. However, the large quantities of unstructured and diversified content generated on these forums make it difficult for users to digest and extract useful information. Understanding the intents of people who post on these forums would enable the retrieval of relevant information from existing threads which would in turn allow users to more effectively find answers to their medical needs. In this paper, we derive a taxonomy of intents to capture user information need in online health forums, and propose novel pattern based features to classify original thread posts according to their underlying intents. Since no dataset existed for this task, we employ three annotators to manually tag a dataset of 1,200 HealthBoards posts spanning four topics. Experimentation finds that pattern based features are highly capable of identifying user intents in forum posts, reaching a precision of 75\\%. In addition, we achieve comparable classification performance by training and testing on posts from different forums, thereby showing the robustness of our method. Finally, we run our trained classifier on a MedHelp dataset to analyze the distribution of intents of different topics in the forum.","abstract_has_math":false,"creators":["Zhang, Thomas"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Zhai, ChengXiang"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2014,"date_issued":"2014-09-16T17:17:16Z","date_published":"2014-09-16T17:17:16Z","updated_at":"2026-07-22T22:25:40Z","subjects":["Intent Classification","Forum Intents","Health Forums"],"languages":["en"],"rights":["Copyright 2014 Thomas Qian-Yun Zhang"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/50426","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Zhai, ChengXiang"]},{"key":"dc:creator","label":"Author","values":["Zhang, Thomas"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2014-09-16T17:17:16Z","2016-09-22T20:59:08Z","2014-08","2014-09-16"]},{"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":["Intent Classification","Forum Intents","Health Forums"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2014 Thomas Qian-Yun Zhang"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/50426"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Online health forums provide a convenient way for patients to obtain medical information and connect with physicians and peers outside of clinical settings. 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In addition, we achieve comparable classification performance by training and testing on posts from different forums, thereby showing the robustness of our method. Finally, we run our trained classifier on a MedHelp dataset to analyze the distribution of intents of different topics in the forum.","Item withdrawn by Mark Zulauf (zulauf@illinois.edu) on 2014-06-16T20:48:19Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 1 Zhang_Thomas.pdf: 146246 bytes, checksum: 734accfdfddddf10d844846892b8b0be (MD5)","Made available in DSpace on 2014-09-16T17:17:16Z (GMT). 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However, the large quantities of unstructured and diversified content generated on these forums make it difficult for users to digest and extract useful information. Understanding the intents of people who post on these forums would enable the retrieval of relevant information from existing threads which would in turn allow users to more effectively find answers to their medical needs. In this paper, we derive a taxonomy of intents to capture user information need in online health forums, and propose novel pattern based features to classify original thread posts according to their underlying intents. Since no dataset existed for this task, we employ three annotators to manually tag a dataset of 1,200 HealthBoards posts spanning four topics. Experimentation finds that pattern based features are highly capable of identifying user intents in forum posts, reaching a precision of 75\\%. 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