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WSDM'16 Relational Learning with Social Status Analysis

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WSDM'16 Relational Learning with Social Status Analysis

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Relational learning with social status analysis
Classify social media users with content- and network-centric features.
Social status of users are leveraged to estimate utility of content from different sources, which is induced from the social network structure.
Keywords: machine learning, graph data mining, social media analytics

Relational learning with social status analysis
Classify social media users with content- and network-centric features.
Social status of users are leveraged to estimate utility of content from different sources, which is induced from the social network structure.
Keywords: machine learning, graph data mining, social media analytics

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WSDM'16 Relational Learning with Social Status Analysis

  1. 1. Relational Learning with Social Status Analysis 1 Arizona State University Data Mining and Machine Learning Lab Relational Learning with Social Status Analysis Liang Wu1, Xia Hu2, and Huan Liu1 Arizona State University1 Texas A&M University2 Acknowledgements This work is sponsored by Office of Naval Research.
  2. 2. Relational Learning with Social Status Analysis 2 Arizona State University Data Mining and Machine Learning Lab Inferring User Labels in Online Social Networks • Infer user labels with user-generated content. Users Content Labels 0 1 1 Training Gender Interests Age • How can we estimate user importance? Model
  3. 3. Relational Learning with Social Status Analysis 3 Arizona State University Data Mining and Machine Learning Lab Inferring User Importance with Network Structures Possible Solution: – Links are available between social network users. – Estimate social status with Link Analysis? a bc d Community A Community BCommunity C e Problems: – Can network centrality represent user importance? • Information loss • Redundancy Solutions: – Constrain the overall influence of large groups.
  4. 4. Relational Learning with Social Status Analysis 4 Arizona State University Data Mining and Machine Learning Lab RESA: Relational Learning with Social Status Analysis Proposed Approach: – Introduce group exclusiveness sparsity regularization. – Effect: Intra-group competition. For each group For each member in a group Individual status a bc d Community A Community BCommunity C Training Labels e Social networks and media content Weighted Regression
  5. 5. Relational Learning with Social Status Analysis 5 Arizona State University Data Mining and Machine Learning Lab Liang Wu http://www.public.asu.edu/~liangwu1/ wuliang@asu.edu RESA: Relational Learning with Social Status Analysis Conclusions: – Reveal social status from online social networks. – Incorporate social status to facilitate relational learning.

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