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However, although these algorithms have performed well on artificial datasets, they do not provide similar results when applied to real-time datasets with high levels of noise and imbalance. Consequently, finding generic algorithms that can work on dynamic data available across several platforms is critical. This study used a unique hybrid random forest-based CNN model for text classification, combining the strengths of both approaches. Real-time datasets from Twitter and Instagram were collected and annotated to demonstrate the effectiveness of the proposed technique. The performance of various ML and DL algorithms was compared, and the RF-based CNN model outperformed them in accuracy and execution speed. This is particularly important for timely detection of bullying episodes and providing assistance to victims. The model achieved an accuracy of 96% and delivered results 3.4 seconds faster than standard CNN models.<\/jats:p>","DOI":"10.3389\/frai.2024.1269366","type":"journal-article","created":{"date-parts":[[2024,3,5]],"date-time":"2024-03-05T23:42:08Z","timestamp":1709682128000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":25,"title":["ProTect: a hybrid deep learning model for proactive detection of cyberbullying on social media"],"prefix":"10.3389","volume":"7","author":[{"given":"T.","family":"Nitya Harshitha","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"M.","family":"Prabu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"E.","family":"Suganya","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"S.","family":"Sountharrajan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Durga Prasad","family":"Bavirisetti","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Navya","family":"Gadde","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lakshmi Sahithi","family":"Uppu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2024,3,6]]},"reference":[{"key":"B1","article-title":"Cyberbullying detection using deep neural network from social media comments in bangla language","author":"Ahmed","year":"2021","journal-title":"arXiv preprint arXiv:2106.04506"},{"key":"B2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/ICAECT49130.2021.9392608","article-title":"\u201cDeployment of machine learning and deep learning algorithms in detecting cyberbullying in bangla and romanized bangla text: a comparative study,\u201d","author":"Ahmed","year":"2021","journal-title":"2021 International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT)"},{"key":"B3","doi-asserted-by":"publisher","first-page":"100027","DOI":"10.1016\/j.nlp.2023.100027","article-title":"A robust hybrid machine learning model for Bengali cyber bullying detection in social media","volume":"4","author":"Akhter","year":"2023","journal-title":"Nat. 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