Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Vinodha, D.; Mary Anita, E.A.; Jenefa, J.; Lapina, Maria Title A name given to the resource Machine Learning Approaches for Detection of Cyberbullying in Virtual Space Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1616 LNNS;pp.104-111 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-3-032-04365-8_9" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-032-04365-8_9</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105019529314?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105019529314?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Vinodha D., Christ University, Bangalore, India; Mary Anita E.A., Christ University, Bangalore, India; Jenefa J., Christ University, Bangalore, India; Lapina M., North-Caucasus Federal University, Stavropol, Russian Federation Description An account of the resource Cyberbullying, hostile behavior of a group or an individual to defame or harass the victim mentally with the help of social media and other e-communication platforms, has the potential to create a lifelong negative impact on mental health with the power of inducing suicidal thoughts. It is on the rise among the early adolescents of the age group from 8 to 16. Hence it is vital to detect Cyberbullying at an early stage to safeguard the victims at the high risk of developing depression, anxiety, and suicidal ideas. It also helps to mitigate psychological, academic, and social consequences. Existing cyberbullying detection approaches primarily depend on static monolingual questionnaires and are not personalised. With the developments in Artificial Intelligence, many neural network-based approaches are explored to detect cyberbullying. This study discusses and provides comparative analysis of various machine learning approaches for detecting cyberbullying victimization among school students highlighting their effectiveness and limitations. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. Subject The topic of the resource Cognitive AI Chatbot; Cyberbullying; Deep Learning; Natural Language Processing Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-303204364-1; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online