Experts and Machines against Bullies: A Hybrid Approach to Detect Cyberbullies

M. Dadvar, Rudolf Berend Trieschnigg, Franciska M.G. de Jong

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    93 Citations (Scopus)
    699 Downloads (Pure)


    Cyberbullying is becoming a major concern in online environments with troubling consequences. However, most of the technical studies have focused on the detection of cyberbullying through identifying harassing comments rather than preventing the incidents by detecting the bullies. In this work we study the automatic detection of bully users on YouTube. We compare three types of automatic detection: an expert system, supervised machine learning models, and a hybrid type combining the two. All these systems assign a score indicating the level of “bulliness‿ of online bullies. We demonstrate that the expert system outperforms the machine learning models. The hybrid classifier shows an even better performance.
    Original languageUndefined
    Title of host publicationProceedings of the 27th Canadian Conference on Artificial Intelligence, Canadian AI 2014
    Place of PublicationBerlin
    Number of pages7
    ISBN (Print)978-3-319-06482-6
    Publication statusPublished - May 2014
    Event27th Canadian Conference on Artificial Intelligence, Canadian AI 2014 - Montreal, Canada
    Duration: 6 May 20149 May 2014

    Publication series

    NameLecture Notes in Computer Science
    PublisherSpringer Verlag
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349


    Conference27th Canadian Conference on Artificial Intelligence, Canadian AI 2014
    Other6-9 May 2014


    • bulliness score
    • EWI-24725
    • IR-91449
    • METIS-305877
    • Cyberbullying
    • Identity identification
    • Expert system

    Cite this