The rapid increase of computer-mediated communications (CMCs) in various forms such as micro-blogging (e.g. Twitter), online chatting (e.g. digital reference) and community- based question-answering services (e.g. Yahoo! Answers) characterizes a recent trend in web technologies, often referred to as the social web. This trend highlights the importance of supporting linguistic interactions in people's online information-seeking activities in daily life - something that the web search engines still lack because of the complexity of this hu- man behavior. The presented research consists of an investigation of the information-seeking behavior of digital reference services through analysis of discourse semantics, called dialogue acts, and experimentation of automatic identification of dialogue acts using machine-learning techniques. The data was an online chat reference transaction archive, provided by the Online Computing Library Center (OCLC). Findings of the discourse analysis include supporting evidence of some of the existing theories of the information-seeking behavior. They also suggest a new way of analyzing the progress of information-seeking interactions using dia- logue act analysis. The machine learning experimentation produced promising results and demonstrated the possibility of practical applications of the DA analysis for further research across disciplines.
","abstract_html":"<p>The rapid increase of computer-mediated communications (CMCs) in various forms such as micro-blogging (e.g. Twitter), online chatting (e.g. digital reference) and community- based question-answering services (e.g. Yahoo! Answers) characterizes a recent trend in web technologies, often referred to as the social web. This trend highlights the importance of supporting linguistic interactions in people's online information-seeking activities in daily life - something that the web search engines still lack because of the complexity of this hu- man behavior. The presented research consists of an investigation of the information-seeking behavior of digital reference services through analysis of discourse semantics, called dialogue acts, and experimentation of automatic identification of dialogue acts using machine-learning techniques. The data was an online chat reference transaction archive, provided by the Online Computing Library Center (OCLC). Findings of the discourse analysis include supporting evidence of some of the existing theories of the information-seeking behavior. They also suggest a new way of analyzing the progress of information-seeking interactions using dia- logue act analysis. The machine learning experimentation produced promising results and demonstrated the possibility of practical applications of the DA analysis for further research across disciplines.</p>","abstract_has_math":false,"creators":["Inoue, Keisuke"],"institution":null,"degree_name":"Doctor of Philosophy (PhD)","degree_level":"Dissertation","degree_discipline":"Information Science and Technology","degree_department":null,"school":null,"contributors":["Elizabeth D. Liddy"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2013,"date_issued":"2013-05-01T07:00:00Z","date_published":"2013-05-01T07:00:00Z","updated_at":"2026-07-24T04:54:37Z","subjects":["dialogue acts","discourse analysis","library","machine learning","online chat","reference","Library and Information Science"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://surface.syr.edu/it_etd/81","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Elizabeth D. Liddy"]},{"key":"dc:creator","label":"Author","values":["Inoue, Keisuke"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"thesis:degree_discipline","label":"Discipline","values":["Information Science and Technology"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy (PhD)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["dialogue acts","discourse analysis","library","machine learning","online chat","reference","Library and Information Science"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://surface.syr.edu/it_etd/81"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The rapid increase of computer-mediated communications (CMCs) in various forms such as micro-blogging (e.g. Twitter), online chatting (e.g. digital reference) and community- based question-answering services (e.g. Yahoo! Answers) characterizes a recent trend in web technologies, often referred to as the social web. This trend highlights the importance of supporting linguistic interactions in people's online information-seeking activities in daily life - something that the web search engines still lack because of the complexity of this hu- man behavior. The presented research consists of an investigation of the information-seeking behavior of digital reference services through analysis of discourse semantics, called dialogue acts, and experimentation of automatic identification of dialogue acts using machine-learning techniques. The data was an online chat reference transaction archive, provided by the Online Computing Library Center (OCLC). Findings of the discourse analysis include supporting evidence of some of the existing theories of the information-seeking behavior. They also suggest a new way of analyzing the progress of information-seeking interactions using dia- logue act analysis. The machine learning experimentation produced promising results and demonstrated the possibility of practical applications of the DA analysis for further research across disciplines.
"]},{"key":"dc:title","label":"Title","values":["An Investigation of Digital Reference Interviews: A Dialogue Act Approach"]}]}],"canonical_facts":{"dc:contributor":["Elizabeth D. Liddy"],"dc:creator":["Inoue, Keisuke"],"dc:description.abstract":["The rapid increase of computer-mediated communications (CMCs) in various forms such as micro-blogging (e.g. Twitter), online chatting (e.g. digital reference) and community- based question-answering services (e.g. Yahoo! Answers) characterizes a recent trend in web technologies, often referred to as the social web. This trend highlights the importance of supporting linguistic interactions in people's online information-seeking activities in daily life - something that the web search engines still lack because of the complexity of this hu- man behavior. The presented research consists of an investigation of the information-seeking behavior of digital reference services through analysis of discourse semantics, called dialogue acts, and experimentation of automatic identification of dialogue acts using machine-learning techniques. The data was an online chat reference transaction archive, provided by the Online Computing Library Center (OCLC). Findings of the discourse analysis include supporting evidence of some of the existing theories of the information-seeking behavior. They also suggest a new way of analyzing the progress of information-seeking interactions using dia- logue act analysis. The machine learning experimentation produced promising results and demonstrated the possibility of practical applications of the DA analysis for further research across disciplines.
"],"dc:identifier":["https://surface.syr.edu/it_etd/81"],"dc:subject":["dialogue acts","discourse analysis","library","machine learning","online chat","reference","Library and Information Science"],"dc:title":["An Investigation of Digital Reference Interviews: A Dialogue Act Approach"],"thesis:degree_discipline":["Information Science and Technology"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Doctor of Philosophy (PhD)"]},"updated_at":"2026-07-24T04:54:37Z"}