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International Conference on Autonomic and Trusted Computing

ATC 2011: Autonomic and Trusted Computing pp 175–186Cite as

Spam Detection on Twitter Using Traditional Classifiers

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Part of the Lecture Notes in Computer Science book series (LNPSE,volume 6906)

Abstract

Social networking sites have become very popular in recent years. Users use them to find new friends, updates their existing friends with their latest thoughts and activities. Among these sites, Twitter is the fastest growing site. Its popularity also attracts many spammers to infiltrate legitimate users’ accounts with a large amount of spam messages. In this paper, we discuss some user-based and content-based features that are different between spammers and legitimate users. Then, we use these features to facilitate spam detection. Using the API methods provided by Twitter, we crawled active Twitter users, their followers/following information and their most recent 100 tweets. Then, we evaluated our detection scheme based on the suggested user and content-based features. Our results show that among the four classifiers we evaluated, the Random Forest classifier produces the best results. Our spam detector can achieve 95.7% precision and 95.7% F-measure using the Random Forest classifier.

Keywords

  • Social network security
  • spam detection
  • machine learning

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  • DOI: 10.1007/978-3-642-23496-5_13
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Author information

Authors and Affiliations

  1. Computer Science & Engineering Department, Lehigh University, Bethlehem, PA, 18015, USA

    M. McCord & M. Chuah

Authors
  1. M. McCord
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  2. M. Chuah
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Editor information

Editors and Affiliations

  1. Cloud and Security Laboratory, Hewlett-Packard Laboratories, BS34 8QZ, Stroke Gifford, United Kingdom

    Jose M. Alcaraz Calero

  2. Department of Computer Science, St. Francis Xavier University, B2G 2W5, Antigonish, NS, Canada

    Laurence T. Yang

  3. Security Laboratory, NEC Laboratories Europe, Kurfürsten-Anlage 36, 69115, Heidelberg, Germany

    Félix Gómez Mármol

  4. Departamento de Ingeniería del Software e Inteligencia, Universidad Complutense de Madrid (UCM), C/Profesor José García Santesmases s/n, 28040, Madrid, Spain

    Luis Javier García Villalba

  5. Department of Electrical and Computer Engineering, University of Florida, 216 Larsen Hll, 32611-6200, Gainesville, FL, USA

    Andy Xiaolin Li

  6. Department of Computing, Macquarie University, E6A339, 2109, Sydney, NSW, Australia

    Yan Wang

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© 2011 Springer-Verlag Berlin Heidelberg

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McCord, M., Chuah, M. (2011). Spam Detection on Twitter Using Traditional Classifiers. In: Calero, J.M.A., Yang, L.T., Mármol, F.G., García Villalba, L.J., Li, A.X., Wang, Y. (eds) Autonomic and Trusted Computing. ATC 2011. Lecture Notes in Computer Science, vol 6906. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23496-5_13

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  • DOI: https://doi.org/10.1007/978-3-642-23496-5_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-23495-8

  • Online ISBN: 978-3-642-23496-5

  • eBook Packages: Computer ScienceComputer Science (R0)