Detection of fake Twitter followers using graph centrality measures

Ashish Mehrotra, Mallidi Sarreddy, Sanjay Singh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

Abstract

With the exponential rise in the number of Internet users, Social Networking platforms have become one of the major means of communication all over the globe. Many major players in this field exist including the likes of Facebook, Twitter, Google+ etc. Impressed by the number of users an individual can reach using the existing Social Networking platforms, most organizations and celebrities make use of them to keep in touch with their fans and followers continuously. Social Networking platforms also allow the organizations or celebrities to publicize any event and to update information regarding their business just to keep themselves active in the market. Most Social Networking platforms provide some form of metric which can be used to define the popularity of an user such as the number of followers on Twitter, number of likes on Facebook etc. However, in recent years, it has been observed that many users attempt to manipulate their popularity metric with the help of fake accounts to look more popular. In this paper, we have devised a method which can be used to detect all the fake followers within a social graph network based on features related to the centrality of all the nodes in the graph and training a classifier based on a subset of the data. Using only graph based centrality measures, the proposed method yielded very high accuracy on fake follower detection. The proposed method is generic in nature and can be used irrespective of the social network platform under consideration.

Original languageEnglish
Title of host publicationProceedings of the 2016 2nd International Conference on Contemporary Computing and Informatics, IC3I 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages499-504
Number of pages6
ISBN (Electronic)9781509052554
DOIs
Publication statusPublished - 01-01-2016
Event2nd International Conference on Contemporary Computing and Informatics, IC3I 2016 - Noida, India
Duration: 14-12-201617-12-2016

Conference

Conference2nd International Conference on Contemporary Computing and Informatics, IC3I 2016
CountryIndia
CityNoida
Period14-12-1617-12-16

    Fingerprint

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Information Systems and Management
  • Health Informatics
  • Computer Networks and Communications
  • Computer Science Applications
  • Information Systems

Cite this

Mehrotra, A., Sarreddy, M., & Singh, S. (2016). Detection of fake Twitter followers using graph centrality measures. In Proceedings of the 2016 2nd International Conference on Contemporary Computing and Informatics, IC3I 2016 (pp. 499-504). [7918016] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IC3I.2016.7918016