Please use this identifier to cite or link to this item: https://doi.org/10.1145/3077136.3080773
Title: Computational Social Indicators: A Case Study of Chinese University Ranking
Authors: Fuli feng 
Liqiang Nie
Xiang Wang
Richang Hong
Tat-Seng Chua 
Keywords: Computational Social Indicators
University Ranking
Issue Date: 7-Aug-2017
Publisher: Association for Computing Machinery, Inc
Citation: Fuli 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. https://doi.org/10.1145/3077136.3080773
Abstract: Many 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).
Source Title: ACM SIGIR 2017
URI: https://scholarbank.nus.edu.sg/handle/10635/167446
ISBN: 9781450350228
DOI: 10.1145/3077136.3080773
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