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Author Champaign, J. ♦ Cohen, R. ♦ Sardana, N. ♦ Doucette, J. A.
Source SpringerLink
Content type Text
Publisher Springer Vienna
File Format PDF
Copyright Year ©2014
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Modeling user trust and credibility ♦ Social Web ♦ Selecting user commentary on web objects ♦ Reducing information overload ♦ Data Mining and Knowledge Discovery ♦ Complex Networks ♦ Game Theory, Economics, Social and Behav. Sciences ♦ Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law ♦ Methodology of the Social Sciences
Abstract In this paper, we present a framework aimed at assisting users in coping with the deluge of information within social networks. We focus on the scenario where a user is trying to digest feedback provided on a Web document (or a video) by peers. In this context, it is ideal for the user to be presented with a restricted view of all the commentary, namely those messages that are most beneficial in increasing the user’s understanding of the document. Operating within the computer science subfield of artificial intelligence, the centerpiece of our approach is a modeling of the trustworthiness of the person leaving commentary (the annotator), determined on the basis of ratings provided by peers, adjusted by a modeling of the similarity of those peers to the current user. We compare three competing formulae for restricting what is shown to users which vary in the extent to which they integrate trust modeling, to emphasize the value of this component. By simulating the knowledge gains achieved by users (inspired by methods used in peer-based intelligent tutoring), we are able to validate the effectiveness of our algorithms. Overall, we offer a framework to make the Social Web a viable source of information, through effective modeling of the credibility of peers. When peers are misguided or deceptive, our approach is able to remove these messages from consideration, for the user.
ISSN 18695450
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2014-06-03
Publisher Place Vienna
e-ISSN 18695469
Journal Social Network Analysis and Mining
Volume Number 4
Issue Number 1
Page Count 15
Starting Page 1
Ending Page 15


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Source: SpringerLink