Computational Social Indicators: A Case Study of Chinese University Ranking

Many professional organizations produce regular reports of social indicators to monitor social progress. Despite their reasonable results and societal value, early efforts on social indicator computing suffer 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. After 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.

[1]  Yixin Chen,et al.  Ranking on Data Manifold with Sink Points , 2013, IEEE Transactions on Knowledge and Data Engineering.

[2]  Rama Chellappa,et al.  Joint Sparse Representation for Robust Multimodal Biometrics Recognition , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Chun Chen,et al.  Music recommendation by unified hypergraph: combining social media information and music content , 2010, ACM Multimedia.

[4]  Michael Bourne,et al.  Difficulties of Developing and Using Social Indicators to Evaluate Government Programs: A critical review , 2002 .

[5]  Tat-Seng Chua,et al.  Micro Tells Macro: Predicting the Popularity of Micro-Videos via a Transductive Model , 2016, ACM Multimedia.

[6]  Dima Shepelyansky,et al.  Wikipedia ranking of world universities , 2015, The European Physical Journal B.

[7]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[8]  Yiqun Liu,et al.  Microblog Sentiment Analysis with Emoticon Space Model , 2014, Journal of Computer Science and Technology.

[9]  Mikhail Belkin,et al.  An iterated graph laplacian approach for ranking on manifolds , 2011, KDD.

[10]  Ming Gao,et al.  BiRank: Towards Ranking on Bipartite Graphs , 2017, IEEE Transactions on Knowledge and Data Engineering.

[11]  Lutz Bornmann,et al.  A new approach to the QS university ranking using the composite I‐distance indicator: Uncertainty and sensitivity analyses , 2016, J. Assoc. Inf. Sci. Technol..

[12]  Tat-Seng Chua,et al.  Unifying Virtual and Physical Worlds , 2017, ACM Trans. Inf. Syst..

[13]  Liang Wang,et al.  Incomplete Multi-view Clustering via Subspace Learning , 2015, CIKM.

[14]  Rajeev Motwani,et al.  The PageRank Citation Ranking : Bringing Order to the Web , 1999, WWW 1999.

[15]  Min-Yen Kan,et al.  Comment-based multi-view clustering of web 2.0 items , 2014, WWW.

[16]  Chunyan Miao,et al.  Learning to name faces: a multimodal learning scheme for search-based face annotation , 2013, SIGIR.

[17]  Asok Ray,et al.  Multimodal Task-Driven Dictionary Learning for Image Classification , 2015, IEEE Transactions on Image Processing.

[18]  Pavel Serdyukov,et al.  On the Relation Between Assessor's Agreement and Accuracy in Gamified Relevance Assessment , 2015, SIGIR.

[19]  Meng Wang,et al.  Multimodal Graph-Based Reranking for Web Image Search , 2012, IEEE Transactions on Image Processing.

[20]  Greg Ridgeway,et al.  Latent Variable Analysis: A New Approach to University Ranking , 2005 .

[21]  Bengt Jönsson,et al.  International comparisons of health expenditure: Theory, data and econometric analysis , 2000 .

[22]  R. Miller,et al.  Measuring Social Well-Being: A Progress Report on the Development of Social Indicators. , 1979 .

[23]  Tat-Seng Chua,et al.  Learning from Collective Intelligence , 2016, ACM Trans. Multim. Comput. Commun. Appl..

[24]  Tie-Yan Liu,et al.  BrowseRank: letting web users vote for page importance , 2008, SIGIR '08.

[25]  Yueting Zhuang,et al.  A low rank structural large margin method for cross-modal ranking , 2013, SIGIR.

[26]  Chris H. Q. Ding,et al.  Multi-document summarization via sentence-level semantic analysis and symmetric matrix factorization , 2008, SIGIR '08.

[27]  Wei-Ying Ma,et al.  Object-level ranking: bringing order to Web objects , 2005, WWW '05.

[28]  Deepak Agarwal,et al.  Ranking Universities Based on Career Outcomes of Graduates , 2016, KDD.

[29]  Sabina Alkire,et al.  A Short Guide to Gross National Happiness Index. , 2012 .

[30]  Shivani Agarwal,et al.  Ranking on graph data , 2006, ICML.

[31]  Jeff A. Bilmes,et al.  On Deep Multi-View Representation Learning , 2015, ICML.

[32]  R. Manmatha,et al.  Automatic image annotation and retrieval using cross-media relevance models , 2003, SIGIR.

[33]  Michael R. Lyu,et al.  Improving Recommender Systems by Incorporating Social Contextual Information , 2011, TOIS.

[34]  Jeff A. Bilmes,et al.  Deep Canonical Correlation Analysis , 2013, ICML.

[35]  Moni Naor,et al.  Rank aggregation methods for the Web , 2001, WWW '01.

[36]  Paul N. Bennett,et al.  Refined experts: improving classification in large taxonomies , 2009, SIGIR.

[37]  Wei Gao,et al.  Democracy is good for ranking: towards multi-view rank learning and adaptation in web search , 2014, WSDM.

[38]  Tat-Seng Chua,et al.  Representativeness-aware Aspect Analysis for Brand Monitoring in Social Media , 2017, IJCAI.

[39]  Juhan Nam,et al.  Multimodal Deep Learning , 2011, ICML.

[40]  Xin Liu,et al.  Document clustering based on non-negative matrix factorization , 2003, SIGIR.

[41]  XiangTao,et al.  Transductive Multi-View Zero-Shot Learning , 2015 .

[42]  Stéphane Marchand-Maillet,et al.  Multiview clustering: a late fusion approach using latent models , 2009, SIGIR.