Skip to main content

Social Context Based Naive Bayes Filtering of Spam Messages from Online Social Networks

  • Conference paper
  • First Online:
Soft Computing in Data Analytics

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 758))

Abstract

Nowadays, online social networking (OSN) sites are an inevitable way of communication. Almost all the OSNs have attained an explosive growth in the last few years. Spammers or hackers found that OSN is an important and easy way to spread spam messages over the network because of its popularity. Spammers use different strategies to spread spam. So, spam detection must be strong enough to detect spam effectively. Though several spam detection techniques are available, it is necessary to increase the accuracy of spam detection techniques. In this paper, a spam detection technique is proposed to detect and prevent spam messages. In addition to the usage of basic classifiers, social context features such as shares, likes, comments (SLC) are also done. Experimental evaluations and comparisons prove that the proposed system with SLC factors provides a higher accuracy than accuracy of basic classifier.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Wang, D., Pu, C.: BEAN: a behaviour analysis approach of URL spam filtering in Twitter. In: International Conference on Information Reuse and Integration, San Francisco, CA, pp. 403–410 (2015)

    Google Scholar 

  2. Chen, C., Zhang, J., Xie, Y., Xiang, Y., Zhou, W., Hassan, M.M., AlElaiwi, A., Alrubaian, M.: A performance evaluation of machine learning based streaming spam tweets detection. IEEE Trans. Comput. Soc. Syst. 2, 65–76 (2015)

    Article  Google Scholar 

  3. Alsaleh, M., Alarifi, A., Al-Quayed, F., Al-Salman, A.: Combating comment spam with machine learning approaches. In: 14th International Conference on Machine Learning and Applications (ICMLA), Miami, pp. 295–300 (2015)

    Google Scholar 

  4. Kim, J.M. Kim, Z.M., Kim, K.: An approach to spam comment detection through domain-independent features. In: International Conference on Big Data and Smart Computing (BigComp), Hong Kong, pp. 273–276 (2016)

    Google Scholar 

  5. Kaur, R., Singh, S.: A survey of data mining and social network analysis based anomaly detection techniques. J. Egypt. Inf. 17, 199–216 (2016)

    Article  Google Scholar 

  6. Chakraborty, M., Pal, S., Ravindranath Chowdary, C., Pramanik, R.: Recent developments in social spam detection and combating techniques. J. Inf. Process. Manag. 52, 1053–1073 (2016)

    Article  Google Scholar 

  7. Yin, R., Wang, H., Liu, L.: Research of integrated algorithm: establishment of spam detection system. In: 4th International Conference on Computer Science and Network Technology (ICCSNT), Harbin, pp. 584–589 (2015)

    Google Scholar 

  8. Zhu, T., Gao, H., Yang, Y., Bu, K., Chen, Y., Downey, D., Lee, K., Choudhary, A.N.: Beating the artificial chaos: fighting OSN spam using its own templates. IEEE/ACM Trans. Netw. 24, 3856–3869 (2016)

    Article  Google Scholar 

  9. Wua, F., Huang, Y., Yuan, Z., Shu, J.: Co-detecting social spammers and spam messages in microblogging via exploiting social contexts. J. Elsevier Neuro Comput. 201, 51–65 (2016)

    Article  Google Scholar 

  10. Hua, J., Huaxiang, Z.: Analysis on the content features and their correlation of web pages for spam detection. IEEE China Commun. 12, 84–94 (2015)

    Google Scholar 

  11. Liu, C., Wang, J., Lei, K.: Detecting spam comments posted in micro—blogs using self-extensible spam dictionary. In: IEEE International Conference on Communications (ICC), Kuala Lumpur, pp. 1–7 (2016)

    Google Scholar 

  12. Song, C., Ge, T.: Window-chained longest common subsequence: common event matching in sequences. In: 31st International Conference on Data Engineering, Seoul, pp. 759–770 (2015)

    Google Scholar 

  13. Vairagade, A.S., Fadnavis, R.A.: Automated content based short text classification for filtering undesired posts on Facebook. In: World Conference on Futuristic Trends in Research and Innovation for Social Welfare (Startup Conclave), Coimbatore, pp. 1–5 (2016)

    Google Scholar 

  14. Gao, L., zhou, S., Guan, J.: Effectively classifying short texts by sparse representation with dictionary filtering. Inf. Sci. J. Elsevier 323, 130–142 (2015)

    Article  MathSciNet  Google Scholar 

  15. Yao, D., Bi, J., Huang, J., Zhu, J.: A word distributed representation based framework for large scale short text classification. In: International Joint Conference on Neural Networks (IJCNN), Killarney, pp. 1–7 (2015)

    Google Scholar 

  16. Cheng, Y., Park, J., Sandhu, R.: An access control model for OSN using user-to-user relationships. IEEE Trans. Dependable Secure Comput. 13, 424–436 (2016)

    Article  Google Scholar 

Download references

Acknowledgements

This work is financially supported under grants provided by the Visvesvaraya Ph.D. Scheme for Electronics and IT.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to C. Valliyammai .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Kiliroor, C.C., Valliyammai, C. (2019). Social Context Based Naive Bayes Filtering of Spam Messages from Online Social Networks. In: Nayak, J., Abraham, A., Krishna, B., Chandra Sekhar, G., Das, A. (eds) Soft Computing in Data Analytics . Advances in Intelligent Systems and Computing, vol 758. Springer, Singapore. https://doi.org/10.1007/978-981-13-0514-6_66

Download citation

Publish with us

Policies and ethics