{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/45309"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/45309","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Advancing sustainability indicators through text mining: a feasibility demonstration","abstract":"Sustainability indicators are metrics that are used to assess and track sustainable development, such as the number of people living in poverty or the conservation status of endangered species. Defining sustainability indicators is challenging because the studies are often expensive and time consuming, the resulting indicators are difficult to track, and they usually have limited social input and acceptance, which is a critical element of the social component of sustainability. The central premise of this work is to explore the feasibility of identifying, tracking and reporting sustainability indicators by analyzing unstructured digital news articles with text mining methods. Using San Mateo County, California, as a case study, a non-mutually exclusive supervised classification algorithm with natural language processing techniques is applied to analyze sustainability content in news articles and compare the results with annual sustainability indicator reports created by Sustainable San Mateo County (SSMC) using traditional methods. The results showed that the text mining approach could identify all of the indicators highlighted as important in the SSMC reports and the method has potential for identifying region-specific sustainability indicators, as well as providing insights on the underlying causes of sustainability problems. Some chronic problems that are considered less newsworthy proved more difficult to track with news articles and may be better monitored using other types of online media, such as blogs and reports. Such use of online media can improve the incorporation of society’s values in the process of selecting and tracking the indicators, a component that had been missing in previous approaches.","abstract_html":"Sustainability indicators are metrics that are used to assess and track sustainable development, such as the number of people living in poverty or the conservation status of endangered species. Defining sustainability indicators is challenging because the studies are often expensive and time consuming, the resulting indicators are difficult to track, and they usually have limited social input and acceptance, which is a critical element of the social component of sustainability. The central premise of this work is to explore the feasibility of identifying, tracking and reporting sustainability indicators by analyzing unstructured digital news articles with text mining methods. Using San Mateo County, California, as a case study, a non-mutually exclusive supervised classification algorithm with natural language processing techniques is applied to analyze sustainability content in news articles and compare the results with annual sustainability indicator reports created by Sustainable San Mateo County (SSMC) using traditional methods. The results showed that the text mining approach could identify all of the indicators highlighted as important in the SSMC reports and the method has potential for identifying region-specific sustainability indicators, as well as providing insights on the underlying causes of sustainability problems. Some chronic problems that are considered less newsworthy proved more difficult to track with news articles and may be better monitored using other types of online media, such as blogs and reports. Such use of online media can improve the incorporation of society’s values in the process of selecting and tracking the indicators, a component that had been missing in previous approaches.","abstract_has_math":false,"creators":["Rivera, Samuel"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Civil Engineering","degree_department":null,"school":null,"contributors":["Minsker, Barbara S."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2013,"date_issued":"2013-08-22T16:35:42Z","date_published":"2013-08-22T16:35:42Z","updated_at":"2026-07-22T22:25:34Z","subjects":["sustainability","indicators","text mining","news media"],"languages":["en"],"rights":["Copyright 2013 Samuel J Rivera"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/45309","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Minsker, Barbara S."]},{"key":"dc:creator","label":"Author","values":["Rivera, Samuel"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2013-08-22T16:35:42Z","2013-08"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Civil Engineering"]},{"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":["sustainability","indicators","text mining","news media"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2013 Samuel J Rivera"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/45309"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Sustainability indicators are metrics that are used to assess and track sustainable development, such as the number of people living in poverty or the conservation status of endangered species. Defining sustainability indicators is challenging because the studies are often expensive and time consuming, the resulting indicators are difficult to track, and they usually have limited social input and acceptance, which is a critical element of the social component of sustainability. The central premise of this work is to explore the feasibility of identifying, tracking and reporting sustainability indicators by analyzing unstructured digital news articles with text mining methods. Using San Mateo County, California, as a case study, a non-mutually exclusive supervised classification algorithm with natural language processing techniques is applied to analyze sustainability content in news articles and compare the results with annual sustainability indicator reports created by Sustainable San Mateo County (SSMC) using traditional methods. The results showed that the text mining approach could identify all of the indicators highlighted as important in the SSMC reports and the method has potential for identifying region-specific sustainability indicators, as well as providing insights on the underlying causes of sustainability problems. Some chronic problems that are considered less newsworthy proved more difficult to track with news articles and may be better monitored using other types of online media, such as blogs and reports. Such use of online media can improve the incorporation of society’s values in the process of selecting and tracking the indicators, a component that had been missing in previous approaches.","Item withdrawn by Mark Zulauf (zulauf@illinois.edu) on 2013-07-16T13:34:36Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 2 Rivera_Samuel.docx: 5007880 bytes, checksum: 163f6d80fc8a3b83b1ebf85255f37386 (MD5) Rivera_Samuel.pdf: 633319 bytes, checksum: f3ebbe7b24e0b2d60545a84bc6bbd44e (MD5)","Made available in DSpace on 2013-08-22T16:35:42Z (GMT). No. of bitstreams: 3 Samuel_Rivera.pdf: 609579 bytes, checksum: 3a61cee5e7807f3ead1c519c5f39369d (MD5) Rivera_Samuel.docx: 3430529 bytes, checksum: eb71172e00aed3004027c0ee77f769ac (MD5) license.txt: 4063 bytes, checksum: b54422ffea942bc0aeae90aaedda9a35 (MD5)"]},{"key":"dc:title","label":"Title","values":["Advancing sustainability indicators through text mining: a feasibility demonstration"]}]}],"canonical_facts":{"dc:contributor":["Minsker, Barbara S."],"dc:creator":["Rivera, Samuel"],"dc:date":["2013-08-22T16:35:42Z","2013-08"],"dc:description":["Sustainability indicators are metrics that are used to assess and track sustainable development, such as the number of people living in poverty or the conservation status of endangered species. Defining sustainability indicators is challenging because the studies are often expensive and time consuming, the resulting indicators are difficult to track, and they usually have limited social input and acceptance, which is a critical element of the social component of sustainability. The central premise of this work is to explore the feasibility of identifying, tracking and reporting sustainability indicators by analyzing unstructured digital news articles with text mining methods. Using San Mateo County, California, as a case study, a non-mutually exclusive supervised classification algorithm with natural language processing techniques is applied to analyze sustainability content in news articles and compare the results with annual sustainability indicator reports created by Sustainable San Mateo County (SSMC) using traditional methods. The results showed that the text mining approach could identify all of the indicators highlighted as important in the SSMC reports and the method has potential for identifying region-specific sustainability indicators, as well as providing insights on the underlying causes of sustainability problems. Some chronic problems that are considered less newsworthy proved more difficult to track with news articles and may be better monitored using other types of online media, such as blogs and reports. Such use of online media can improve the incorporation of society’s values in the process of selecting and tracking the indicators, a component that had been missing in previous approaches.","Item withdrawn by Mark Zulauf (zulauf@illinois.edu) on 2013-07-16T13:34:36Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 2 Rivera_Samuel.docx: 5007880 bytes, checksum: 163f6d80fc8a3b83b1ebf85255f37386 (MD5) Rivera_Samuel.pdf: 633319 bytes, checksum: f3ebbe7b24e0b2d60545a84bc6bbd44e (MD5)","Made available in DSpace on 2013-08-22T16:35:42Z (GMT). No. of bitstreams: 3 Samuel_Rivera.pdf: 609579 bytes, checksum: 3a61cee5e7807f3ead1c519c5f39369d (MD5) Rivera_Samuel.docx: 3430529 bytes, checksum: eb71172e00aed3004027c0ee77f769ac (MD5) license.txt: 4063 bytes, checksum: b54422ffea942bc0aeae90aaedda9a35 (MD5)"],"dc:identifier":["http://hdl.handle.net/2142/45309"],"dc:language":["en"],"dc:rights":["Copyright 2013 Samuel J Rivera"],"dc:subject":["sustainability","indicators","text mining","news media"],"dc:title":["Advancing sustainability indicators through text mining: a feasibility demonstration"],"dc:type":["text"],"thesis:degree_discipline":["Civil Engineering"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:34Z"}