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dc.titleComputational Social Indicators: A Case Study of Chinese University Ranking
dc.contributor.authorFuli feng
dc.contributor.authorLiqiang Nie
dc.contributor.authorXiang Wang
dc.contributor.authorRichang Hong
dc.contributor.authorTat-Seng Chua
dc.identifier.citationFuli feng, Liqiang Nie, Xiang Wang, Richang Hong, Tat-Seng Chua (2017-08-07). Computational Social Indicators: A Case Study of Chinese University Ranking. ACM SIGIR 2017 : 455-464. ScholarBank@NUS Repository.
dc.description.abstractMany professional organizations produce regular reports of social indicators to monitor social progress. Despite their reasonable results and societal value, early e'orts on social indicator computing su'er from three problems: 1) labor-intensive data gathering, 2) insufficient data, and 3) expert-relied data fusion. Towards this end, we present a novel graph-based multi-channel ranking scheme for social indicator computation by exploring the rich multi-channel Web data. For each channel, this scheme presents the semi-structured and unstructured data with simple graphs and hypergraphs, respectively. It then groups the channels into different clusters according to their correlations. Affter that, it uses a unified model to learn the cluster-wise common spaces, perform ranking separately upon each space, and fuse these rankings to produce the final one. We take Chinese university ranking as a case study and validate our scheme over a real-world dataset. It is worth emphasizing that our scheme is applicable to computation of other social indicators, such as Educational attainment. © 2017 Copyright held by the owner/author(s).
dc.publisherAssociation for Computing Machinery, Inc
dc.subjectComputational Social Indicators
dc.subjectUniversity Ranking
dc.typeConference Paper
dc.contributor.departmentDEPT OF COMPUTER SCIENCE
dc.description.sourcetitleACM SIGIR 2017
dc.grant.fundingagencyInfocomm Media Development Authority
dc.grant.fundingagencyNational Research Foundation
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