{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/cyberbullying-detection-in-social-networks","title":"Cyberbullying Detection in Social Networks Using Deep Learning Based Models; A Reproducibility Study","arxiv_id":"1812.08046","date":"2018-12-19","proceeding":null,"authors":["Maral Dadvar","Kai Eckert"],"abstract":"Cyberbullying is a disturbing online misbehaviour with troubling\nconsequences. It appears in different forms, and in most of the social\nnetworks, it is in textual format. Automatic detection of such incidents\nrequires intelligent systems. Most of the existing studies have approached this\nproblem with conventional machine learning models and the majority of the\ndeveloped models in these studies are adaptable to a single social network at a\ntime. In recent studies, deep learning based models have found their way in the\ndetection of cyberbullying incidents, claiming that they can overcome the\nlimitations of the conventional models, and improve the detection performance.\nIn this paper, we investigate the findings of a recent literature in this\nregard. We successfully reproduced the findings of this literature and\nvalidated their findings using the same datasets, namely Wikipedia, Twitter,\nand Formspring, used by the authors. Then we expanded our work by applying the\ndeveloped methods on a new YouTube dataset (~54k posts by ~4k users) and\ninvestigated the performance of the models in new social media platforms. We\nalso transferred and evaluated the performance of the models trained on one\nplatform to another platform. Our findings show that the deep learning based\nmodels outperform the machine learning models previously applied to the same\nYouTube dataset. We believe that the deep learning based models can also\nbenefit from integrating other sources of information and looking into the\nimpact of profile information of the users in social networks.","url_abs":"http://arxiv.org/abs/1812.08046v1","url_pdf":"http://arxiv.org/pdf/1812.08046v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"cyberbullying-detection-in-social-networks","repo_url":"https://github.com/sweta20/Detecting-Cyberbullying-Across-SMPs","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"4k","task_name":"4k"},{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"deep-learning","task_name":"Deep Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1812.08046","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}