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

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

    36 Citations (Scopus)
    201 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
    Dadvar, M. ; Trieschnigg, Rudolf Berend ; de Jong, Franciska M.G. / Experts and Machines against Bullies: A Hybrid Approach to Detect Cyberbullies. Proceedings of the 27th Canadian Conference on Artificial Intelligence, Canadian AI 2014. Berlin : Springer, 2014. pp. 275-281 (Lecture Notes in Computer Science).
    @inproceedings{cc6a32718a9041eca2b073fda290f008,
    title = "Experts and Machines against Bullies: A Hybrid Approach to Detect Cyberbullies",
    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.",
    keywords = "bulliness score, EWI-24725, IR-91449, METIS-305877, Cyberbullying, Identity identification, Expert system",
    author = "M. Dadvar and Trieschnigg, {Rudolf Berend} and {de Jong}, {Franciska M.G.}",
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    Dadvar, M, Trieschnigg, RB & de Jong, FMG 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. Lecture Notes in Computer Science, vol. 8436, Springer, Berlin, pp. 275-281. https://doi.org/10.1007/978-3-319-06483-3_25

    Experts and Machines against Bullies: A Hybrid Approach to Detect Cyberbullies. / Dadvar, M.; Trieschnigg, Rudolf Berend; de Jong, Franciska M.G.

    Proceedings of the 27th Canadian Conference on Artificial Intelligence, Canadian AI 2014. Berlin : Springer, 2014. p. 275-281 (Lecture Notes in Computer Science; Vol. 8436).

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

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    AB - 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.

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