Detection of hate: speech tweets based convolutional neural network and machine learning algorithms (scientific reports)

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