Combining textual features to detect cyberbullying in social media posts
dc.contributor.author | Fortunatus, M. | |
dc.contributor.author | Anthony, Patricia | |
dc.contributor.author | Charters, Stuart | |
dc.date.accessioned | 2021-01-26T01:44:45Z | |
dc.date.available | 2020-10-02 | en |
dc.date.issued | 2020 | |
dc.description.abstract | Cyberbullying 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.extent | 612-621 | en |
dc.identifier.doi | 10.1016/j.procs.2020.08.063 | en |
dc.identifier.eissn | 1877-0509 | en |
dc.identifier.issn | 1877-0509 | en |
dc.identifier.uri | https://hdl.handle.net/10182/13278 | |
dc.language.iso | en | |
dc.publisher | Elsevier | |
dc.relation | The original publication is available from - Elsevier - https://doi.org/10.1016/j.procs.2020.08.063 - http://kes2020.kesinternational.org/ | en |
dc.relation.isPartOf | Procedia Computer Science | en |
dc.relation.uri | https://doi.org/10.1016/j.procs.2020.08.063 | en |
dc.rights | © 2020 The Authors. Published by Elsevier B.V. | |
dc.rights.ccname | Attribution-NonCommercial-NoDerivatives | en |
dc.rights.ccuri | https://creativecommons.org/licenses/by-nc-nd/4.0/ | en |
dc.source | 24th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems | en |
dc.subject | cyberbullying | en |
dc.subject | cyber aggression | en |
dc.subject | textual aggression | en |
dc.subject | sentiment analysis | en |
dc.subject | emoticon sentiment | en |
dc.subject | emoji sentiment | en |
dc.subject | aggression detection | en |
dc.subject.anzsrc | ANZSRC::170203 Knowledge Representation and Machine Learning | en |
dc.subject.anzsrc | ANZSRC::200101 Communication Studies | en |
dc.subject.anzsrc | ANZSRC::200102 Communication Technology and Digital Media Studies | en |
dc.subject.anzsrc | ANZSRC::209999 Language, Communication and Culture not elsewhere classified | en |
dc.subject.anzsrc | ANZSRC::200408 Linguistic Structures (incl. Grammar, Phonology, Lexicon, Semantics) | en |
dc.title | Combining textual features to detect cyberbullying in social media posts | en |
dc.type | Conference Contribution - published | |
lu.contributor.unit | Lincoln University | |
lu.contributor.unit | Faculty of Environment, Society and Design | |
lu.contributor.unit | School of Landscape Architecture | |
lu.identifier.orcid | 0000-0002-4991-3340 | |
lu.identifier.orcid | 0000-0002-1560-0805 | |
lu.subtype | Conference Paper | en |
pubs.finish-date | 2020-09-18 | en |
pubs.notes | Conference held online | en |
pubs.publication-status | Published | en |
pubs.publisher-url | http://kes2020.kesinternational.org/ | en |
pubs.start-date | 2020-09-16 | en |
pubs.volume | 176 | en |
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