RUcore: Rutgers University Community Repository
Extracting users in community question-answering in particular contexts
Descriptive
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Title
Extracting users in community question-answering in particular contexts
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Le
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Long T.
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1985-
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Long T. Le
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author
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Shah
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Chirag Shah
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Advisory Committee
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chair
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Marian
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Amelie
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Amelie Marian
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Advisory Committee
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internal member
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Nguyen
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Thu D
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Thu D Nguyen
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Advisory Committee
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internal member
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Panos
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Panos Ipeirotis
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Advisory Committee
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Rutgers University
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School of Graduate Studies
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theses
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2017
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2017-10
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2017
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xx
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eng
Abstract
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Community Question-Answering (CQA) services, such as Yahoo! Answers, Stack Overflow and Brainly, have become important sources of seeking and sharing information. Online users use CQA to look for information and share knowledge on topics ranging from arts to travel. The questions posted on CQA sites often rely on the wisdom of the crowd, or the idea that the best answer could come from a culmination of several answers by different people with varying expertise and opinions. Given that CQA is a user-driven service, user experience becomes an important aspect, affecting the activeness and even the survival of the site. In this work, we are interested in studying the behavior of the users who participate in CQA. Specifically, we wish to understand how different types of users could be identified based on their behaviors on a CQA-specific problem at hand. A user's behavior depends on their particular context. For example, when we say that Alice is a "good user," the interpretation of her behavior actually rests on the context in which it occurs. She might be a good user in the whole community, a good user for a specific topic, a good user for a particular question or a good user for a particular answer. In this dissertation, we will study and extract users in different levels of granularity. Users are the main driving force in CQA and understanding them allows us to know the current state of their respective sites. The findings in this dissertation will be useful in identifying specific CQA user types.
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Computer Science
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Rutgers University Electronic Theses and Dissertations
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ETD_8202
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electronic resource
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1 online resource (xiii, 118 p. : ill.)
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Ph.D.
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Includes bibliographical references
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by Long T. Le
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School of Graduate Studies Electronic Theses and Dissertations
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rucore10001600001
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NjNbRU
Identifier
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doi:10.7282/T3GQ71WD
Genre
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ETD doctoral
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Rights
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The author owns the copyright to this work.
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Name
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Le
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Long
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T.
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Permission or license
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2017-05-08 20:55:57
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Long Le
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Rutgers University. School of Graduate Studies
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Author Agreement License
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I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
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2017-10-31
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2018-05-02
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Detail
Access to this PDF has been restricted at the author's request. It will be publicly available after May 2nd, 2018.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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