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

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

    47 Citations (Scopus)
    304 Downloads (Pure)

    Abstract

    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
    PublisherSpringer
    Pages275-281
    Number of pages7
    ISBN (Print)978-3-319-06482-6
    DOIs
    Publication statusPublished - May 2014

    Publication series

    NameLecture Notes in Computer Science
    PublisherSpringer Verlag
    Volume8436
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Keywords

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

    Cite this

    Dadvar, M., Trieschnigg, R. B., & de Jong, F. M. G. (2014). Experts and Machines against Bullies: A Hybrid Approach to Detect Cyberbullies. In Proceedings of the 27th Canadian Conference on Artificial Intelligence, Canadian AI 2014 (pp. 275-281). (Lecture Notes in Computer Science; Vol. 8436). Berlin: Springer. https://doi.org/10.1007/978-3-319-06483-3_25