The article addresses a method for resolving this issue by feature engineering eleven classifiers for machine and deep learning that can automatically detect hate speech using a Term Frequency-Inverse Document Frequency (TF-IDF) based methodology. Davidson et al.’s”Hatespeech obnoxious tweets” was the first database utilized, followed by “Twitter hate speech” and lastly “Cyberbullying dataset (toxicity_parsed_dataset)” which we combined.” In addition to the convolutional neural network (CNN), the classifiers used include the following: Random Forest (RF), K-Nearest Neighbor (KNN), K-Means, Decision Tree (DT), Gradient Boosting classifier (GBC), Support Vector Machine (SVM), Logistic Regression (LR), Naive Bayes (NB), Multi-layer Perceptron (MLP), and the Extra Trees (ET). The highest accuracy of almost 99% was achieved. https://www.nature.com/articles/s41598-024-76632-2 Share this: Click to print (Opens in new window) Print Click to share on Facebook (Opens in new window) Facebook Click to share on LinkedIn (Opens in new window) LinkedIn Click to share on Reddit (Opens in new window) Reddit Click to share on WhatsApp (Opens in new window) WhatsApp Click to share on Bluesky (Opens in new window) Bluesky Click to email a link to a friend (Opens in new window) Email Like this:Like Loading... Post navigation The Virality of Hate Speech on Social Media (arXiv) Two Weeks in Soft Security: Free Resources on Countering Extremism, Hate, and Disinformation, November 2024 (II/II)