Supervised Machine Learning for the Detection of Troll Profiles in Twitter Social Network: Application to a Real Case of Cyberbullying

  • Patxi Galán-García
  • José Gaviria de la Puerta
  • Carlos Laorden Gómez
  • Igor Santos
  • Pablo García Bringas
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 239)

Abstract

The use of new technologies along with the popularity of social networks has given the power of anonymity to the users. The ability to create an alter-ego with no relation to the actual user, creates a situation in which no one can certify the match between a profile and a real person. This problem generates situations, repeated daily, in which users with fake accounts, or at least not related to their real identity, publish news, reviews or multimedia material trying to discredit or attack other people who may or may not be aware of the attack. These acts can have great impact on the affected victims’ environment generating situations in which virtual attacks escalate into fatal consequences in real life. In this paper, we present a methodology to detect and associate fake profiles on Twitter social network which are employed for defamatory activities to a real profile within the same network by analysing the content of comments generated by both profiles. Accompanying this approach we also present a successful real life use case in which this methodology was applied to detect and stop a cyberbullying situation in a real elementary school.

Keywords

On-line Social Networks Trolling Information Retrieval Identity Theft Cyberbullying 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Patxi Galán-García
    • 1
  • José Gaviria de la Puerta
    • 1
  • Carlos Laorden Gómez
    • 1
  • Igor Santos
    • 1
  • Pablo García Bringas
    • 1
  1. 1.DeustoTech ComputingUniversity of DeustoDonostia-San SebastiánSpain

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