{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/113860"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/113860","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"From user-generated text to insight context-aware measurement of social impacts and interactions using natural language processing","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-04-06 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-04-06 without embargo terms","abstract_has_math":false,"creators":["Rezapour, Rezvaneh"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Information Sciences","degree_department":null,"school":null,"contributors":["Diesner, Jana","Underwood, Ted","Girju, Roxana","Karahalios, Karrie"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-04-29T21:34:31Z","date_published":"2022-04-29T21:34:31Z","updated_at":"2026-07-22T22:24:53Z","subjects":["Information sciences"],"languages":["en","eng"],"rights":["Copyright 2021 Rezvaneh Rezapour"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/113860","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Diesner, Jana","Underwood, Ted","Girju, Roxana","Karahalios, Karrie"]},{"key":"dc:creator","label":"Author","values":["Rezapour, Rezvaneh"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-04-29T21:34:31Z","2021-12","2021-11-29"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Information Sciences"]},{"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":["Information sciences"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2021 Rezvaneh Rezapour"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/113860"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-04-06 without embargo terms","The student, Rezvaneh Rezapour, accepted the attached license on 2021-11-25 at 12:09.","The student, Rezvaneh Rezapour, submitted this Dissertation for approval on 2021-11-25 at 12:43.","This Dissertation was approved for publication on 2021-11-29 at 10:55.","DSpace SAF Submission Ingestion Package generated from Vireo submission #17267 on 2022-04-06 at 17:10:03","Made available in DSpace on 2022-04-29T21:34:31Z (GMT). No. of bitstreams: 2 REZAPOUR-DISSERTATION-2021.pdf: 12479551 bytes, checksum: 2ece551e7574bc2a5d4c28c2f295d3da (MD5) LICENSE.txt: 4211 bytes, checksum: 9e3eb92f6b1e41977c95d9b7a22f44f1 (MD5) Previous issue date: 2021-11-29","\"Recent improvements in information and communication technologies have contributed to an increasingly globalized and connected world. The digital data that are created as the result of people's online activities and interactions consist of different types of personal and social information that can be used to extract and understand people's implicit or explicit beliefs, ideas, and biases. This thesis leverages methods and theories from natural language processing and social sciences to study and analyze the manifestations of various attributes and signals, namely social impacts, personal values, and moral traits, in user-generated texts. This work provides a comprehensive understanding of people's viewpoints, social values, and interactions and makes the following contributions. First, we present a study that combines review mining and impact assessment to provide an extensive discussion on different types of impact that information products, namely documentary films, can have on people. We first establish a novel impact taxonomy and demonstrate that, with a rigorous analysis of user-generated texts and a theoretically grounded codebook, classification schema, and prediction model, we can detect multiple types of (self-reported) impact in texts and show that people's language can help in gaining insights about their opinions, socio-cultural information, and emotional states. Furthermore, the results of our analyses show that documentary films can shift peoples' perceptions and cognitions regarding different societal issues, e.g., climate change, and using a combination of informative features (linguistic, syntactic, and psychological), we can predict impact in sentences with high accuracy. Second, we investigate the relationship between principles of human morality and the expression of stances in user-generated text data, namely tweets. More specifically, we first introduce and expand the Moral Foundations Dictionary and operationalize moral values to enhance the measurement of social effects. In addition, we provide detailed explanation on how morality and stance are associated in user-generated texts. Through extensive analysis, we show that discussions related to various social issues have distinctive moral and lexical profiles, and leveraging moral values as an additional feature can lead to measurable improvements in prediction accuracy of stance analysis. Third, we utilize the representation of emotional and moral states in texts to study people's interactions in two different social networks. Moreover, we first expand the analysis of structural balance to include direction and multi-level balance assessment (triads, subgroups, and the whole network). Our results show that analyzing different levels of networks and using various linguistic cues can grant a more inclusive view of people and the stability of their interactions; we found that, unlike sentiments, moral statuses in discussions stay balanced throughout the networks even in the presence of tension. Overall, this thesis aims to contribute to the emerging field of \"\"social\"\" NLP and broadens the scope of research in it by (1) utilizing a combination of novel taxonomies, datasets, and tools to examine user-generated texts and (2) providing more comprehensive insights about human language, cultures, and experiences.\""]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["From user-generated text to insight context-aware measurement of social impacts and interactions using natural language processing"]}]}],"canonical_facts":{"dc:contributor":["Diesner, Jana","Underwood, Ted","Girju, Roxana","Karahalios, Karrie"],"dc:creator":["Rezapour, Rezvaneh"],"dc:date":["2022-04-29T21:34:31Z","2021-12","2021-11-29"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-04-06 without embargo terms","The student, Rezvaneh Rezapour, accepted the attached license on 2021-11-25 at 12:09.","The student, Rezvaneh Rezapour, submitted this Dissertation for approval on 2021-11-25 at 12:43.","This Dissertation was approved for publication on 2021-11-29 at 10:55.","DSpace SAF Submission Ingestion Package generated from Vireo submission #17267 on 2022-04-06 at 17:10:03","Made available in DSpace on 2022-04-29T21:34:31Z (GMT). No. of bitstreams: 2 REZAPOUR-DISSERTATION-2021.pdf: 12479551 bytes, checksum: 2ece551e7574bc2a5d4c28c2f295d3da (MD5) LICENSE.txt: 4211 bytes, checksum: 9e3eb92f6b1e41977c95d9b7a22f44f1 (MD5) Previous issue date: 2021-11-29","\"Recent improvements in information and communication technologies have contributed to an increasingly globalized and connected world. The digital data that are created as the result of people's online activities and interactions consist of different types of personal and social information that can be used to extract and understand people's implicit or explicit beliefs, ideas, and biases. This thesis leverages methods and theories from natural language processing and social sciences to study and analyze the manifestations of various attributes and signals, namely social impacts, personal values, and moral traits, in user-generated texts. This work provides a comprehensive understanding of people's viewpoints, social values, and interactions and makes the following contributions. First, we present a study that combines review mining and impact assessment to provide an extensive discussion on different types of impact that information products, namely documentary films, can have on people. We first establish a novel impact taxonomy and demonstrate that, with a rigorous analysis of user-generated texts and a theoretically grounded codebook, classification schema, and prediction model, we can detect multiple types of (self-reported) impact in texts and show that people's language can help in gaining insights about their opinions, socio-cultural information, and emotional states. Furthermore, the results of our analyses show that documentary films can shift peoples' perceptions and cognitions regarding different societal issues, e.g., climate change, and using a combination of informative features (linguistic, syntactic, and psychological), we can predict impact in sentences with high accuracy. Second, we investigate the relationship between principles of human morality and the expression of stances in user-generated text data, namely tweets. More specifically, we first introduce and expand the Moral Foundations Dictionary and operationalize moral values to enhance the measurement of social effects. In addition, we provide detailed explanation on how morality and stance are associated in user-generated texts. Through extensive analysis, we show that discussions related to various social issues have distinctive moral and lexical profiles, and leveraging moral values as an additional feature can lead to measurable improvements in prediction accuracy of stance analysis. Third, we utilize the representation of emotional and moral states in texts to study people's interactions in two different social networks. Moreover, we first expand the analysis of structural balance to include direction and multi-level balance assessment (triads, subgroups, and the whole network). Our results show that analyzing different levels of networks and using various linguistic cues can grant a more inclusive view of people and the stability of their interactions; we found that, unlike sentiments, moral statuses in discussions stay balanced throughout the networks even in the presence of tension. Overall, this thesis aims to contribute to the emerging field of \"\"social\"\" NLP and broadens the scope of research in it by (1) utilizing a combination of novel taxonomies, datasets, and tools to examine user-generated texts and (2) providing more comprehensive insights about human language, cultures, and experiences.\""],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/2142/113860"],"dc:language":["en","eng"],"dc:rights":["Copyright 2021 Rezvaneh Rezapour"],"dc:subject":["Information sciences"],"dc:title":["From user-generated text to insight context-aware measurement of social impacts and interactions using natural language processing"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Information Sciences"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:53Z"}