{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/104858"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/104858","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Influence mining from unstructured big data","abstract":"A crucial component of any intelligent system is to understand and predict the behavior of its users. A correct model of user's behavior enables the system to perform effectively to better serve the user's need. While much work has been done on user behavior modeling based on historical activity data, little attention has been paid to how external factors influence the user behavior, which is clearly important for improving an intelligent system. The influence of external factors on user behavior is mostly reflected in two different ways: 1) through significant growth of users' thirst about information related to external factors (e.g., the user may conduct many searches related to a popular event or related to some community of interest), and 2) through user-generated content that are directly/indirectly related to the external factors (e.g. the user may tweet about a particular event). To capture these two aspects of user behavior, I introduce Influence Models for both Information Thirst and Content Generation, sequentially, in this thesis. To the best of my knowledge, Influence models for Information Thirst and Content Generation have not been studied before. The thesis starts with the introduction of a new data mining problem, i.e., how to mine the influence of real world events on users' information thirst, which is important both for social science research and for designing better search engines for users. I solve this mining problem by proposing computational measures that quantify the influence of an event on a query to identify triggered queries and then, proposing a novel extension of Hawkes process to model the evolutionary trend of the influence of an event on search queries. Evaluation results using news articles and search log data show that the proposed approach is effective for identification of queries triggered by events reported in news articles and characterization of the influence trend over time. This influence model assumes that each event poses its influence independently. This assumption is unrealistic as there are many correlated events in the real world which influence each other and thus, would influence the user search behavior jointly rather than independently. To relax this assumption, in the next part of my thesis, I propose a Joint Influence Model based on the Multivariate Hawkes Process which captures the interdependence among multiple events in terms of their influence. Experimental study shows that the Joint Influence Model achieves higher accuracy than the independent model. The second way to observe external influence on user behavior is to analyze user-generated content that is directly/indirectly related to those external factors, which I discuss in the last part of the thesis. For example, user-generated content is often significantly influenced by the community to which the user belongs to. While some work has been done on mining such influence from structured information networks, little attention has been paid on how to mine community-influence from user generated unstructured data. To study such influence, I introduce the problem of mining community-influence from user-generated unstructured contents, particularly in the context of text content generation. Although text generation has recently become a popular research topic after the surge of deep learning techniques, existing methods do not consider community-influence factor into the generation process and thus, the processes do not evolve over time. This clearly limits their application on text stream data as most text stream data often evolve over time showing distinct patterns corresponding to the shifting interests of the target community. To address this limitation, I propose an Influenced Text Generation (ITG) Process that can capture this evolution of text generation process corresponding to evolving community-influence over time. ITG is based on deep learning architecture and uses LSTM cells within the hidden layers of a recurrent neural network. Experimental results with six independent text stream data comprised of conference paper titles show that the proposed ITG method is really effective in capturing the influences of different research communities on paper titles generated by the researchers.","abstract_html":"A crucial component of any intelligent system is to understand and predict the behavior of its users. A correct model of user's behavior enables the system to perform effectively to better serve the user's need. While much work has been done on user behavior modeling based on historical activity data, little attention has been paid to how external factors influence the user behavior, which is clearly important for improving an intelligent system. The influence of external factors on user behavior is mostly reflected in two different ways: 1) through significant growth of users' thirst about information related to external factors (e.g., the user may conduct many searches related to a popular event or related to some community of interest), and 2) through user-generated content that are directly/indirectly related to the external factors (e.g. the user may tweet about a particular event). To capture these two aspects of user behavior, I introduce Influence Models for both Information Thirst and Content Generation, sequentially, in this thesis. To the best of my knowledge, Influence models for Information Thirst and Content Generation have not been studied before. The thesis starts with the introduction of a new data mining problem, i.e., how to mine the influence of real world events on users' information thirst, which is important both for social science research and for designing better search engines for users. I solve this mining problem by proposing computational measures that quantify the influence of an event on a query to identify triggered queries and then, proposing a novel extension of Hawkes process to model the evolutionary trend of the influence of an event on search queries. Evaluation results using news articles and search log data show that the proposed approach is effective for identification of queries triggered by events reported in news articles and characterization of the influence trend over time. This influence model assumes that each event poses its influence independently. This assumption is unrealistic as there are many correlated events in the real world which influence each other and thus, would influence the user search behavior jointly rather than independently. To relax this assumption, in the next part of my thesis, I propose a Joint Influence Model based on the Multivariate Hawkes Process which captures the interdependence among multiple events in terms of their influence. Experimental study shows that the Joint Influence Model achieves higher accuracy than the independent model. The second way to observe external influence on user behavior is to analyze user-generated content that is directly/indirectly related to those external factors, which I discuss in the last part of the thesis. For example, user-generated content is often significantly influenced by the community to which the user belongs to. While some work has been done on mining such influence from structured information networks, little attention has been paid on how to mine community-influence from user generated unstructured data. To study such influence, I introduce the problem of mining community-influence from user-generated unstructured contents, particularly in the context of text content generation. Although text generation has recently become a popular research topic after the surge of deep learning techniques, existing methods do not consider community-influence factor into the generation process and thus, the processes do not evolve over time. This clearly limits their application on text stream data as most text stream data often evolve over time showing distinct patterns corresponding to the shifting interests of the target community. To address this limitation, I propose an Influenced Text Generation (ITG) Process that can capture this evolution of text generation process corresponding to evolving community-influence over time. ITG is based on deep learning architecture and uses LSTM cells within the hidden layers of a recurrent neural network. Experimental results with six independent text stream data comprised of conference paper titles show that the proposed ITG method is really effective in capturing the influences of different research communities on paper titles generated by the researchers.","abstract_has_math":false,"creators":["Karmaker Santu, Shubhra Kanti"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Zhai, ChengXiang","Han, Jiawei","Sundaram, Hari","Ma, Hao"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019-08-23T19:55:43Z","date_published":"2019-08-23T19:55:43Z","updated_at":"2026-07-22T22:24:42Z","subjects":["Influence Mining","Hawkes Process","User Behavior Modeling","Text Generation","Community Influence","Evolving Text Stream","Unstructured Data","Event Analysis"],"languages":["en"],"rights":["Copyright 2019, Shubhra Kanti Karmaker Santu"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/104858","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Zhai, ChengXiang","Han, Jiawei","Sundaram, Hari","Ma, Hao"]},{"key":"dc:creator","label":"Author","values":["Karmaker Santu, Shubhra Kanti"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2019-08-23T19:55:43Z","2019-04-17","2019-05"]},{"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":["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":["Influence Mining","Hawkes Process","User Behavior Modeling","Text Generation","Community Influence","Evolving Text Stream","Unstructured Data","Event Analysis"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2019, Shubhra Kanti Karmaker Santu"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/104858"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["A crucial component of any intelligent system is to understand and predict the behavior of its users. A correct model of user's behavior enables the system to perform effectively to better serve the user's need. While much work has been done on user behavior modeling based on historical activity data, little attention has been paid to how external factors influence the user behavior, which is clearly important for improving an intelligent system. The influence of external factors on user behavior is mostly reflected in two different ways: 1) through significant growth of users' thirst about information related to external factors (e.g., the user may conduct many searches related to a popular event or related to some community of interest), and 2) through user-generated content that are directly/indirectly related to the external factors (e.g. the user may tweet about a particular event). To capture these two aspects of user behavior, I introduce Influence Models for both Information Thirst and Content Generation, sequentially, in this thesis. To the best of my knowledge, Influence models for Information Thirst and Content Generation have not been studied before. The thesis starts with the introduction of a new data mining problem, i.e., how to mine the influence of real world events on users' information thirst, which is important both for social science research and for designing better search engines for users. I solve this mining problem by proposing computational measures that quantify the influence of an event on a query to identify triggered queries and then, proposing a novel extension of Hawkes process to model the evolutionary trend of the influence of an event on search queries. Evaluation results using news articles and search log data show that the proposed approach is effective for identification of queries triggered by events reported in news articles and characterization of the influence trend over time. This influence model assumes that each event poses its influence independently. This assumption is unrealistic as there are many correlated events in the real world which influence each other and thus, would influence the user search behavior jointly rather than independently. To relax this assumption, in the next part of my thesis, I propose a Joint Influence Model based on the Multivariate Hawkes Process which captures the interdependence among multiple events in terms of their influence. Experimental study shows that the Joint Influence Model achieves higher accuracy than the independent model. The second way to observe external influence on user behavior is to analyze user-generated content that is directly/indirectly related to those external factors, which I discuss in the last part of the thesis. For example, user-generated content is often significantly influenced by the community to which the user belongs to. While some work has been done on mining such influence from structured information networks, little attention has been paid on how to mine community-influence from user generated unstructured data. To study such influence, I introduce the problem of mining community-influence from user-generated unstructured contents, particularly in the context of text content generation. Although text generation has recently become a popular research topic after the surge of deep learning techniques, existing methods do not consider community-influence factor into the generation process and thus, the processes do not evolve over time. This clearly limits their application on text stream data as most text stream data often evolve over time showing distinct patterns corresponding to the shifting interests of the target community. To address this limitation, I propose an Influenced Text Generation (ITG) Process that can capture this evolution of text generation process corresponding to evolving community-influence over time. ITG is based on deep learning architecture and uses LSTM cells within the hidden layers of a recurrent neural network. Experimental results with six independent text stream data comprised of conference paper titles show that the proposed ITG method is really effective in capturing the influences of different research communities on paper titles generated by the researchers.","Submission original under an indefinite embargo labeled 'Open Access'. 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While much work has been done on user behavior modeling based on historical activity data, little attention has been paid to how external factors influence the user behavior, which is clearly important for improving an intelligent system. The influence of external factors on user behavior is mostly reflected in two different ways: 1) through significant growth of users' thirst about information related to external factors (e.g., the user may conduct many searches related to a popular event or related to some community of interest), and 2) through user-generated content that are directly/indirectly related to the external factors (e.g. the user may tweet about a particular event). To capture these two aspects of user behavior, I introduce Influence Models for both Information Thirst and Content Generation, sequentially, in this thesis. To the best of my knowledge, Influence models for Information Thirst and Content Generation have not been studied before. The thesis starts with the introduction of a new data mining problem, i.e., how to mine the influence of real world events on users' information thirst, which is important both for social science research and for designing better search engines for users. I solve this mining problem by proposing computational measures that quantify the influence of an event on a query to identify triggered queries and then, proposing a novel extension of Hawkes process to model the evolutionary trend of the influence of an event on search queries. Evaluation results using news articles and search log data show that the proposed approach is effective for identification of queries triggered by events reported in news articles and characterization of the influence trend over time. This influence model assumes that each event poses its influence independently. This assumption is unrealistic as there are many correlated events in the real world which influence each other and thus, would influence the user search behavior jointly rather than independently. To relax this assumption, in the next part of my thesis, I propose a Joint Influence Model based on the Multivariate Hawkes Process which captures the interdependence among multiple events in terms of their influence. Experimental study shows that the Joint Influence Model achieves higher accuracy than the independent model. The second way to observe external influence on user behavior is to analyze user-generated content that is directly/indirectly related to those external factors, which I discuss in the last part of the thesis. For example, user-generated content is often significantly influenced by the community to which the user belongs to. While some work has been done on mining such influence from structured information networks, little attention has been paid on how to mine community-influence from user generated unstructured data. To study such influence, I introduce the problem of mining community-influence from user-generated unstructured contents, particularly in the context of text content generation. Although text generation has recently become a popular research topic after the surge of deep learning techniques, existing methods do not consider community-influence factor into the generation process and thus, the processes do not evolve over time. This clearly limits their application on text stream data as most text stream data often evolve over time showing distinct patterns corresponding to the shifting interests of the target community. To address this limitation, I propose an Influenced Text Generation (ITG) Process that can capture this evolution of text generation process corresponding to evolving community-influence over time. ITG is based on deep learning architecture and uses LSTM cells within the hidden layers of a recurrent neural network. Experimental results with six independent text stream data comprised of conference paper titles show that the proposed ITG method is really effective in capturing the influences of different research communities on paper titles generated by the researchers.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2019-08-22 without embargo terms","The student, Shubhra Kanti Karmaker Santu, accepted the attached license on 2019-04-17 at 12:10.","The student, Shubhra Kanti Karmaker Santu, submitted this Dissertation for approval on 2019-04-17 at 12:22.","This Dissertation was approved for publication on 2019-04-17 at 14:30.","DSpace SAF Submission Ingestion Package generated from Vireo submission #13714 on 2019-08-22 at 14:44:33","Made available in DSpace on 2019-08-23T19:55:43Z (GMT). No. of bitstreams: 3 KARMAKERSANTU-DISSERTATION-2019.pdf: 4529492 bytes, checksum: 2790c81340c6e74d1a14892096168730 (MD5) LICENSE.txt: 4225 bytes, checksum: 8f2ae6af12fb1105126a0fefaab91d02 (MD5) PROQUEST_LICENSE.txt: 4571 bytes, checksum: 79f627fd5eadf49cc1d28f3eebc8c2db (MD5) Previous issue date: 2019-04-17"],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/2142/104858"],"dc:language":["en"],"dc:rights":["Copyright 2019, Shubhra Kanti Karmaker Santu"],"dc:subject":["Influence Mining","Hawkes Process","User Behavior Modeling","Text Generation","Community Influence","Evolving Text Stream","Unstructured Data","Event Analysis"],"dc:title":["Influence mining from unstructured big data"],"dc:type":["text"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:42Z"}