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Attribute selection for improving spam classification in online social networks: a rough set theory-based approach

  • Soumi Dutta
  • Sujata Ghatak
  • Ratnadeep Dey
  • Asit Kumar Das
  • Saptarshi Ghosh
Original Article

Abstract

As online social network (OSN) sites become increasingly popular, they are targeted by spammers who post malicious content on the sites. Hence, it is important to filter out spam accounts and spam posts from OSNs. There exist several prior works on spam classification on OSNs, which utilize various features to distinguish between spam and legitimate entities. The objective of this study is to improve such spam classification, by developing an attribute selection methodology that helps to find a smaller subset of the attributes which leads to better classification. Specifically, we apply the concepts of rough set theory to develop the attribute selection algorithm. We perform experiments over five different spam classification datasets over diverse OSNs and compare the performance of the proposed methodology with that of several baseline methodologies for attribute selection. We find that, for most of the datasets, the proposed methodology selects an attribute subset that is smaller than what is selected by the baseline methodologies, yet achieves better classification performance compared to the other methods.

Keywords

Online social networks Spam detection Classification Attribute selection Rough set theory 

Notes

Acknowledgements

We thank the anonymous reviewers for their valuable comments and suggestions, which helped to improve the paper. We also acknowledge useful discussions with Arpan Das and Anirban Majumder in the early phases of the work.

Supplementary material

13278_2017_484_MOESM1_ESM.csv (31 mb)
Supplementary material 1 (csv 31725 KB)

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Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2018

Authors and Affiliations

  • Soumi Dutta
    • 1
    • 2
  • Sujata Ghatak
    • 2
  • Ratnadeep Dey
    • 1
  • Asit Kumar Das
    • 1
  • Saptarshi Ghosh
    • 1
    • 3
  1. 1.Department of Computer Science and TechnologyIndian Institute of Engineering Science and TechnologyShibpurIndia
  2. 2.Department of Computer Science and EngineeringInstitute of Engineering & ManagementKolkataIndia
  3. 3.Department of Computer Science and EngineeringIndian Institute of TechnologyKharagpurIndia

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