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Predicting User Relationship from Scratch

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Social Media Processing (SMP 2015)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 568))

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Abstract

A task of primary importance for social network users is to differentiate whom and what to trust among large information. The trustworthiness of the users is often tantamount to the reliability of the information they provide. In this paper we focus on automatic methods for assessing the credibility of a given pair of users. Specifically, we establish a model to classify them as credible or not credible, based on features extracted from them. We test our model on three real world dataset Epinions, Slashdot and Wikipedia, the results indicate that although we only knew tiny information about a user, we can infer their relationship with higher accuracy compare to other researchers.

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Acknowledgments

This work is partially supported by grant from the Natural Science Foundation of China (No. 61277370, 61572102), Natural Science Foundation of Liaoning Province, China (No. 201202031, 2014020003), State Education Ministry and The Research Fund for the Doctoral Program of Higher Education (No. 20090041110002).

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Authors and Affiliations

  1. School of Computer Science and Technology, Dalian University of Technology, Dalian, 116024, Liaoning, China

    Xiaoxia Liu, Hongfei Lin & Zhihao Yang

Authors
  1. Xiaoxia Liu
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  2. Hongfei Lin
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  3. Zhihao Yang
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Corresponding author

Correspondence to Xiaoxia Liu .

Editor information

Editors and Affiliations

  1. South China University of Technology, Guangzhou, China

    Xichun Zhang

  2. Tsinghua University, Beijing, China

    Maosong Sun

  3. South China University of Technology, Guangzhou, China

    Zhenyu Wang

  4. Fudan University, Shanghai, China

    Xuanjing Huang

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© 2015 Springer Science+Business Media Singapore

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Liu, X., Lin, H., Yang, Z. (2015). Predicting User Relationship from Scratch. In: Zhang, X., Sun, M., Wang, Z., Huang, X. (eds) Social Media Processing. SMP 2015. Communications in Computer and Information Science, vol 568. Springer, Singapore. https://doi.org/10.1007/978-981-10-0080-5_16

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  • DOI: https://doi.org/10.1007/978-981-10-0080-5_16

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-0079-9

  • Online ISBN: 978-981-10-0080-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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