Session-based cyberbullying detection in social media: A survey
dc.contributor.author | Yi, P | en_US |
dc.contributor.author | Zubiaga, A | en_US |
dc.date.accessioned | 2023-07-20T10:44:48Z | |
dc.date.available | 2023-05-24 | en_US |
dc.date.issued | 2023-06-17 | en_US |
dc.identifier.issn | 2468-6964 | en_US |
dc.identifier.uri | https://qmro.qmul.ac.uk/xmlui/handle/123456789/89663 | |
dc.description.abstract | Cyberbullying is a pervasive problem in online social media, where a bully abuses a victim through a social media session. By investigating cyberbullying perpetrated through social media sessions, recent research has looked into mining patterns and features for modelling and understanding the two defining characteristics of cyberbullying: repetitive behaviour and power imbalance. In this survey paper, we define a framework that encapsulates four different steps session-based cyberbullying detection should go through, and discuss the multiple challenges that differ from single text-based cyberbullying detection. Based on this framework, we provide a comprehensive overview of session-based cyberbullying detection in social media, delving into existing efforts from a data and methodological perspective. Our review leads us to proposing evidence-based criteria for a set of best practices to create session-based cyberbullying datasets. In addition, we perform benchmark experiments comparing the performance of state-of-the-art session-based cyberbullying detection models as well as large pre-trained language models across two different datasets. Through our review, we also put forth a set of open challenges as future research directions. | en_US |
dc.publisher | Elsevier | en_US |
dc.relation.ispartof | Online Social Media and Networks | en_US |
dc.rights | This item is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. | |
dc.rights | Attribution 3.0 United States | * |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/us/ | * |
dc.title | Session-based cyberbullying detection in social media: A survey | en_US |
dc.type | Article | |
dc.rights.holder | © 2023, The Author(s). Published by Elsevier | |
dc.identifier.doi | 10.1016/j.osnem.2023.100250 | en_US |
pubs.notes | Not known | en_US |
pubs.publication-status | Published | en_US |
pubs.volume | 36 | en_US |
dcterms.dateAccepted | 2023-05-24 | en_US |
rioxxterms.funder | Default funder | en_US |
rioxxterms.identifier.project | Default project | en_US |
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Except where otherwise noted, this item's license is described as This item is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.