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Site agnostic approach to early detection of cyberbullying on social media networks
dc.contributor.author | López-Vizcaíno, Manuel F. | |
dc.contributor.author | Nóvoa, Francisco | |
dc.contributor.author | Artieres, Thierry | |
dc.contributor.author | Cacheda, Fidel | |
dc.date.accessioned | 2023-10-27T07:49:36Z | |
dc.date.available | 2023-10-27T07:49:36Z | |
dc.date.issued | 2023-05 | |
dc.identifier.citation | M. López-Vizcaíno, F.J. Nóvoa, T. Artieres, and F. Cacheda, "Site Agnostic Approach to Early Detection of Cyberbullying on Social Media Networks", Sensors 2023, 23, 4788. https://doi.org/10.3390/s23104788 | es_ES |
dc.identifier.uri | http://hdl.handle.net/2183/33949 | |
dc.description.abstract | [Abstract]: The rise in the use of social media networks has increased the prevalence of cyberbullying, and time is paramount to reduce the negative effects that derive from those behaviours on any social media platform. This paper aims to study the early detection problem from a general perspective by carrying out experiments over two independent datasets (Instagram and Vine), exclusively using users’ comments. We used textual information from comments over baseline early detection models (fixed, threshold, and dual models) to apply three different methods of improving early detection. First, we evaluated the performance of Doc2Vec features. Finally, we also presented multiple instance learning (MIL) on early detection models and we assessed its performance. We applied (Formula presented.) ((Formula presented.)) as an early detection metric to asses the performance of the presented methods. We conclude that the inclusion of Doc2Vec features improves the performance of baseline early detection models by up to 79.6%. Moreover, multiple instance learning shows an important positive effect for the Vine dataset, where smaller post sizes and less use of the English language are present, with a further improvement of up to 13%, but no significant enhancement is shown for the Instagram dataset. | es_ES |
dc.description.sponsorship | This research was supported by the Ministry of Economy and Competitiveness of Spain and FEDER funds of the European Union (Project PID2019-111388GB-I00) and by the Centro de Investigación de Galicia “CITIC”, funded by Xunta de Galicia and the European Union (European Regional Development Fund-Galicia 2014-2020 Program), by grant ED431G 2019/01. | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431G 2019/01 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | MDPI | es_ES |
dc.relation | eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-111388GB-I00/ES/DETECCION TEMPRANA DE INTRUSIONES Y ANOMALIAS EN REDES DEFINIDAS POR SOFTWARE | es_ES |
dc.relation.uri | https://doi.org/10.3390/s23104788 | es_ES |
dc.rights | Attribution 4.0 International | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.subject | Cyberbullying | es_ES |
dc.subject | Early detection | es_ES |
dc.subject | Machine learning | es_ES |
dc.subject | Multiple-instance learning | es_ES |
dc.subject | Social networks | es_ES |
dc.subject | Text features | es_ES |
dc.title | Site agnostic approach to early detection of cyberbullying on social media networks | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | Sensors | es_ES |
UDC.volume | 23 | es_ES |
UDC.issue | 10 | es_ES |
dc.identifier.doi | 10.3390/s23104788 |