{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/117823"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/117823","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Hierarchical regression model tree for explainable actor segmentation and response prediction on social networks","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-04-12 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-04-12 without embargo terms","abstract_has_math":false,"creators":["Li, Jinning"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Abdelzaher, Tarek"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-12","date_published":"2022-12","updated_at":"2026-07-22T22:24:56Z","subjects":["Social Network Analysis","Regression Model Tree","Response Prediction","Machine Learning"],"languages":["en","eng"],"rights":["Copyright 2022 Jinning Li"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/117823","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Abdelzaher, Tarek"]},{"key":"dc:creator","label":"Author","values":["Li, Jinning"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-12","2022-12-05"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"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 Network Analysis","Regression Model Tree","Response Prediction","Machine Learning"]}]},{"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 2022 Jinning Li"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/117823"]}]},{"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 2023-04-12 without embargo terms","The student, Jinning Li, accepted the attached license on 2022-12-01 at 23:51.","The student, Jinning Li, submitted this Thesis for approval on 2022-12-02 at 01:18.","This Thesis was approved for publication on 2022-12-05 at 13:06.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18731 on 2023-04-12 at 07:37:04","Social network systems have produced large-scale data of social signals. However, the potential mechanism of social signal propagation and how it affects people's beliefs and responses are still not well investigated. In this project, we propose a framework and an explainable Hierarchical Regression Model Tree (HRMT) algorithm to solve the individual-level and segmentation-level response prediction tasks and therefore provide the solution to analyze how people's morality, demographics, and other psychographic characteristics affect their beliefs and response to the social information influence. We develop a text-based actor enrichment prediction module based on the Bidirectional Encoder Representations from Transformers (BERT) language model and predict the message enrichment with a weakly-supervised topic detection model. The Hierarchical Regression Model Tree is constructed with regression-error greedy search and reliability test algorithms and then used to construct the segments of actors based on tree structure and predict future responses. These results can be applied for many downstream researches and tasks, such as sociological analysis, influence campaign detection, advertisement, and recommender systems. We also proposed two novel evaluation metrics, normalized segment Discounted Cumulative Gain (nsDCG) and invariant nsDCG. Experimental evaluations show the proposed HRMT outperforms the state-of-the-art models by 0.12 in the nsDCG metrics. We also introduce the application of HRMT in analyzing the characteristics of actors' beliefs based on the tree structure."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Hierarchical regression model tree for explainable actor segmentation and response prediction on social networks"]}]}],"canonical_facts":{"dc:contributor":["Abdelzaher, Tarek"],"dc:creator":["Li, Jinning"],"dc:date":["2022-12","2022-12-05"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-04-12 without embargo terms","The student, Jinning Li, accepted the attached license on 2022-12-01 at 23:51.","The student, Jinning Li, submitted this Thesis for approval on 2022-12-02 at 01:18.","This Thesis was approved for publication on 2022-12-05 at 13:06.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18731 on 2023-04-12 at 07:37:04","Social network systems have produced large-scale data of social signals. However, the potential mechanism of social signal propagation and how it affects people's beliefs and responses are still not well investigated. In this project, we propose a framework and an explainable Hierarchical Regression Model Tree (HRMT) algorithm to solve the individual-level and segmentation-level response prediction tasks and therefore provide the solution to analyze how people's morality, demographics, and other psychographic characteristics affect their beliefs and response to the social information influence. We develop a text-based actor enrichment prediction module based on the Bidirectional Encoder Representations from Transformers (BERT) language model and predict the message enrichment with a weakly-supervised topic detection model. The Hierarchical Regression Model Tree is constructed with regression-error greedy search and reliability test algorithms and then used to construct the segments of actors based on tree structure and predict future responses. These results can be applied for many downstream researches and tasks, such as sociological analysis, influence campaign detection, advertisement, and recommender systems. We also proposed two novel evaluation metrics, normalized segment Discounted Cumulative Gain (nsDCG) and invariant nsDCG. Experimental evaluations show the proposed HRMT outperforms the state-of-the-art models by 0.12 in the nsDCG metrics. We also introduce the application of HRMT in analyzing the characteristics of actors' beliefs based on the tree structure."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/117823"],"dc:language":["en","eng"],"dc:rights":["Copyright 2022 Jinning Li"],"dc:subject":["Social Network Analysis","Regression Model Tree","Response Prediction","Machine Learning"],"dc:title":["Hierarchical regression model tree for explainable actor segmentation and response prediction on social networks"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:56Z"}