Combining textual features to detect cyberbullying in social media posts

dc.contributor.authorFortunatus, M.
dc.contributor.authorAnthony, Patricia
dc.contributor.authorCharters, Stuart
dc.date.accessioned2021-01-26T01:44:45Z
dc.date.available2020-10-02en
dc.date.issued2020
dc.description.abstractCyberbullying has become prevalent in social media communication. To create a safe space for cyber communication, an effective cyberbullying detection method is needed. This study focuses on using combination of textual features to detect cyberbullying across social media platforms. Lexicon enhanced rule-based method was applied to detect cyberbullying on Facebook comments. The resulting algorithm was evaluated using performance measures of accuracy, precision, recall, and F1 Score, and showed promising performance with average recall of 95.981%.en
dc.format.extent612-621en
dc.identifier.doi10.1016/j.procs.2020.08.063en
dc.identifier.eissn1877-0509en
dc.identifier.issn1877-0509en
dc.identifier.urihttps://hdl.handle.net/10182/13278
dc.language.isoen
dc.publisherElsevier
dc.relationThe original publication is available from - Elsevier - https://doi.org/10.1016/j.procs.2020.08.063 - http://kes2020.kesinternational.org/en
dc.relation.isPartOfProcedia Computer Scienceen
dc.relation.urihttps://doi.org/10.1016/j.procs.2020.08.063en
dc.rights© 2020 The Authors. Published by Elsevier B.V.
dc.rights.ccnameAttribution-NonCommercial-NoDerivativesen
dc.rights.ccurihttps://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.source24th International Conference on Knowledge-Based and Intelligent Information & Engineering Systemsen
dc.subjectcyberbullyingen
dc.subjectcyber aggressionen
dc.subjecttextual aggressionen
dc.subjectsentiment analysisen
dc.subjectemoticon sentimenten
dc.subjectemoji sentimenten
dc.subjectaggression detectionen
dc.subject.anzsrcANZSRC::170203 Knowledge Representation and Machine Learningen
dc.subject.anzsrcANZSRC::200101 Communication Studiesen
dc.subject.anzsrcANZSRC::200102 Communication Technology and Digital Media Studiesen
dc.subject.anzsrcANZSRC::209999 Language, Communication and Culture not elsewhere classifieden
dc.subject.anzsrcANZSRC::200408 Linguistic Structures (incl. Grammar, Phonology, Lexicon, Semantics)en
dc.titleCombining textual features to detect cyberbullying in social media postsen
dc.typeConference Contribution - published
lu.contributor.unitLincoln University
lu.contributor.unitFaculty of Environment, Society and Design
lu.contributor.unitSchool of Landscape Architecture
lu.identifier.orcid0000-0002-4991-3340
lu.identifier.orcid0000-0002-1560-0805
lu.subtypeConference Paperen
pubs.finish-date2020-09-18en
pubs.notesConference held onlineen
pubs.publication-statusPublisheden
pubs.publisher-urlhttp://kes2020.kesinternational.org/en
pubs.start-date2020-09-16en
pubs.volume176en
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