Community question answering (CQA) forums can provide effective means for sharing information and addressing a user's information needs about particular topics. However, many such online forums are not moderated, resulting in many low quality and redundant comments, which makes it very challenging for users to find the appropriate answers to their questions. In this paper, we apply a user-centered design approach to develop a system, CQAVis, which supports users in identifying high quality comments and get their questions answered. Informed by the user's requirements, the system combines both text analytics and interactive visualization techniques together in a synergistic way. Given a new question posed by the user, the text analytic module automatically finds relevant answers by exploring existing related questions and the comments within their threads. Then the visualization module presents the search results to the user and supports the exploration of related comments. We have evaluated the system in the wild by deploying it within a CQA forum among thousands of real users. Through the online study, we gained deeper insights about the potential utility of the system, as well as learned generalizable lessons for designing visual text analytics systems for the domain of CQA forums.
Proceedings of the 22Nd International Conference on Intelligent User Interfaces
год
2017
страницы
161--172
издательство
ACM
серии
IUI '17
citeulike-article-id
14310740
isbn
978-1-4503-4348-0
citeulike-linkout-1
http://dx.doi.org/10.1145/3025171.3025210
priority
2
posted-at
2017-03-14 12:00:43
citeulike-linkout-0
http://portal.acm.org/citation.cfm?id=3025210
comment
(private-note)Model of recommendation in QA systems - you ask a question, system finds some related questions and attempts to evaluate which answers are most relevant. It is shown through simple visualization
Relevant to our tasks of recommendation in Classroom Salon
%0 Conference Paper
%1 citeulike:14310740
%A Hoque, Enamul
%A Joty, Shafiq
%A Marquez, Luis
%A Carenini, Giuseppe
%B Proceedings of the 22Nd International Conference on Intelligent User Interfaces
%C New York, NY, USA
%D 2017
%I ACM
%K forum information-visualization iui2017 jbpaws recommender
%P 161--172
%R 10.1145/3025171.3025210
%T CQAVis: Visual Text Analytics for Community Question Answering
%U http://dx.doi.org/10.1145/3025171.3025210
%X Community question answering (CQA) forums can provide effective means for sharing information and addressing a user's information needs about particular topics. However, many such online forums are not moderated, resulting in many low quality and redundant comments, which makes it very challenging for users to find the appropriate answers to their questions. In this paper, we apply a user-centered design approach to develop a system, CQAVis, which supports users in identifying high quality comments and get their questions answered. Informed by the user's requirements, the system combines both text analytics and interactive visualization techniques together in a synergistic way. Given a new question posed by the user, the text analytic module automatically finds relevant answers by exploring existing related questions and the comments within their threads. Then the visualization module presents the search results to the user and supports the exploration of related comments. We have evaluated the system in the wild by deploying it within a CQA forum among thousands of real users. Through the online study, we gained deeper insights about the potential utility of the system, as well as learned generalizable lessons for designing visual text analytics systems for the domain of CQA forums.
%@ 978-1-4503-4348-0
@inproceedings{citeulike:14310740,
abstract = {{Community question answering (CQA) forums can provide effective means for sharing information and addressing a user's information needs about particular topics. However, many such online forums are not moderated, resulting in many low quality and redundant comments, which makes it very challenging for users to find the appropriate answers to their questions. In this paper, we apply a user-centered design approach to develop a system, CQAVis, which supports users in identifying high quality comments and get their questions answered. Informed by the user's requirements, the system combines both text analytics and interactive visualization techniques together in a synergistic way. Given a new question posed by the user, the text analytic module automatically finds relevant answers by exploring existing related questions and the comments within their threads. Then the visualization module presents the search results to the user and supports the exploration of related comments. We have evaluated the system in the wild by deploying it within a CQA forum among thousands of real users. Through the online study, we gained deeper insights about the potential utility of the system, as well as learned generalizable lessons for designing visual text analytics systems for the domain of CQA forums.}},
added-at = {2018-03-19T12:24:51.000+0100},
address = {New York, NY, USA},
author = {Hoque, Enamul and Joty, Shafiq and Marquez, Luis and Carenini, Giuseppe},
biburl = {https://www.bibsonomy.org/bibtex/224a32c30230139c1accc4f7d524c6b62/aho},
booktitle = {Proceedings of the 22Nd International Conference on Intelligent User Interfaces},
citeulike-article-id = {14310740},
citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=3025210},
citeulike-linkout-1 = {http://dx.doi.org/10.1145/3025171.3025210},
comment = {(private-note)Model of recommendation in QA systems - you ask a question, system finds some related questions and attempts to evaluate which answers are most relevant. It is shown through simple visualization
Relevant to our tasks of recommendation in Classroom Salon},
doi = {10.1145/3025171.3025210},
interhash = {991f4edff20a3e5f8907089b72ec3b9a},
intrahash = {24a32c30230139c1accc4f7d524c6b62},
isbn = {978-1-4503-4348-0},
keywords = {forum information-visualization iui2017 jbpaws recommender},
location = {Limassol, Cyprus},
pages = {161--172},
posted-at = {2017-03-14 12:00:43},
priority = {2},
publisher = {ACM},
series = {IUI '17},
timestamp = {2018-03-19T12:24:51.000+0100},
title = {{CQAVis: Visual Text Analytics for Community Question Answering}},
url = {http://dx.doi.org/10.1145/3025171.3025210},
year = 2017
}