NDLI: Exploring Question Selection Bias to Identify Experts and Potential Experts in Community Question Answering

Content Provider	ACM Digital Library
Author	Pal, Aditya Konstan, Joseph A. Harper, F. Maxwell
Copyright Year	2012
Abstract	Community Question Answering (CQA) services enable their users to exchange knowledge in the form of questions and answers. These communities thrive as a result of a small number of highly active users, typically called $\textit{experts},$ who provide a large number of high-quality useful answers. Expert identification techniques enable community managers to take measures to retain the experts in the community. There is further value in identifying the experts during the first few weeks of their participation as it would allow measures to nurture and retain them. In this article we address two problems: (a) How to identify current experts in CQA? and (b) How to identify users who have potential of becoming experts in future (potential experts)? In particular, we propose a probabilistic model that captures the selection preferences of users based on the questions they choose for answering. The probabilistic model allows us to run machine learning methods for identifying experts and potential experts. Our results over several popular CQA datasets indicate that experts differ considerably from ordinary users in their selection preferences; enabling us to predict experts with higher accuracy over several baseline models. We show that selection preferences can be combined with baseline measures to improve the predictive performance even further.
Starting Page	1
Ending Page	28
Page Count	28
File Format	PDF
ISSN	10468188
e-ISSN	15582868
DOI	10.1145/2180868.2180872
Volume Number	30
Issue Number	2
Journal	ACM Transactions on Information Systems (TOIS)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2012-05-01
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Expert identification Community question answering Question selection process
Content Type	Text
Resource Type	Article
Subject	Computer Science Applications Information Systems Business, Management and Accounting

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

A exploring question selection bias to identify experts and potential experts in community question answering.

Expert identification in community question answering: exploring question selection bias

Approaches to Exploring Category Information for Question Retrieval in Community Question-Answer Archives

Expert identification in community question answering: Exploring question selection bias (2010)

Social Question Answering: Textual, User, and Network Features for Best Answer Prediction

Metrics and Algorithms for Routing Questions to User Communities

Soft pattern matching models for definitional question answering

Probabilistic models for answer-ranking in multilingual question-answering

Sibyl, a factoid question-answering system for spoken documents

Exploring Question Selection Bias to Identify Experts and Potential Experts in Community Question Answering

Similar Documents

A exploring question selection bias to identify experts and potential experts in community question answering.

Expert identification in community question answering: exploring question selection bias

Approaches to Exploring Category Information for Question Retrieval in Community Question-Answer Archives

Expert identification in community question answering: Exploring question selection bias (2010)

Social Question Answering: Textual, User, and Network Features for Best Answer Prediction

Metrics and Algorithms for Routing Questions to User Communities

Soft pattern matching models for definitional question answering

Probabilistic models for answer-ranking in multilingual question-answering

Sibyl, a factoid question-answering system for spoken documents

Exploring Question Selection Bias to Identify Experts and Potential Experts in Community Question Answering