Advertisement

Securing Trust in Online Social Networks

  • Vishnu S. PendyalaEmail author
Conference paper
  • 30 Downloads
Part of the Communications in Computer and Information Science book series (CCIS, volume 1186)

Abstract

Trust in Online Social Networks (OSN) is a contentious topic. On one hand, there is an increasing reliance on them for trustworthy information and on the other, wariness to believe anything on it. Although the providers of OSNs have tried multiple ways to boost the trustworthiness of the information posted on their websites and weed out millions of fake accounts, the problem is largely unsolved and poses a formidable challenge. This paper examines the problem is some detail, discusses existing solutions to the problem using Machine Learning and other techniques and concludes by discussing some more ideas on enhancing the trustworthiness of the OSNs.

Keywords

Online Social Networks Machine Learning Trust management 

Notes

Acknowledgement

The author acknowledges the help from his student, Ajith N. in doing some initial work for this paper.

References

  1. 1.
    Pendyala, V.: Veracity of Big Data: Machine Learning and Other Approaches to Verifying Truthfulness, 1st edn. Apress, USA (2018)CrossRefGoogle Scholar
  2. 2.
    Richardson, M., Agrawal, R., Domingos, P.: Trust management for the semantic web. In: Fensel, D., Sycara, K., Mylopoulos, J. (eds.) ISWC 2003. LNCS, vol. 2870, pp. 351–368. Springer, Heidelberg (2003).  https://doi.org/10.1007/978-3-540-39718-2_23CrossRefGoogle Scholar
  3. 3.
    Nuñez-Gonzalez, D., Graña, M., Apolloni, B.: Reputation features for trust prediction in social networks. Neurocomputing 166, 1–7 (2014)CrossRefGoogle Scholar
  4. 4.
    Zhu, Y., Wang, X., Zhong, E., Liu, N., Li, H., Yang, Q.: Discovering spammers in social networks. In: Association for the Advancement of Artificial Intelligence Conference (2012)Google Scholar
  5. 5.
    Zheng, X., Zeng, Z., Chen, Z., Yu, Y., Rong, C.: Detecting spammers in social networks. Neurocomputing 159, 27–34 (2015)CrossRefGoogle Scholar
  6. 6.
    Markines, B., Cattuto, C., Menczer, F.: Social spam detection. ACM 978-1-60558-438-6 (2009)Google Scholar
  7. 7.
    Fire, M., Kagan, D., Elyashar, A., Elovici, Y.: Friend or foe? Fake profile identification in online social networks. arXiv:1303.3751v1 (2013)
  8. 8.
    Xiao, C., Freeman, D., Hwa, T.: Detecting clusters of fake accounts in online social networks. ACM (2015). ISBN 978-1-4503-3826-4/15/10Google Scholar
  9. 9.
    Pendyala, V.S., Liu, Y., Figueira, S.M.: A framework for detecting injected influence attacks on microblog websites using change detection techniques. Dev. Eng. 3, 218–233 (2018)CrossRefGoogle Scholar
  10. 10.
    Pendyala, V.S., Figueira, S.: Towards a truthful world wide web from a humanitarian perspective. In: 2015 IEEE Global Humanitarian Technology Conference (GHTC), October 8, pp. 137–143. IEEE (2015)Google Scholar
  11. 11.
    Bodnar, T., Tucker, C., Hopkinson, K., Bilen, S.: Increasing the veracity of event detection on online social networks through user trust modeling. In: Proceedings of the 2014 IEEE International Conference on Big Data, Washington D.C. (2014)Google Scholar
  12. 12.
    Chakravorty, A., Chunming R.: Ushare: user controlled social media based on blockchain. In: Proceedings of the 11th International Conference on Ubiquitous Information Management and Communication, p. 99. ACM (2017)Google Scholar
  13. 13.
    Senapati, M., Laurent, N., Praveen, R.: A method for scalable first-order rule learning on Twitter data. In: Proceedings of the 2019 IEEE 35th International Conference on Data Engineering Workshops (ICDEW), pp. 274–277. IEEE (2019)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.San Jose State UniversitySan JoseUSA

Personalised recommendations