Hate speech detection in social media apps using deep learning and machine learning techniques - Paredes Benavides, Jimmy Gerardo (2025)
This work presents a model able to detect if a tweet contains hate speech or not, using as primary data tweets collected from the X platform in the context of the 2023 Chilean Plebiscite Constitutional Reform. Machine learning and deep learning approaches were used to obtain the best model and Natural Language Processing techniques to process the text data. Since the dataset used presents an imbalance in its classes, an analysis of the use of data augmentation and data reduction was performed to find out which of those techniques performs better in this dataset. It was concluded that the data augmentation technique was useful in this work because of the low number of samples on the dataset for one of its classes, but the data reduction did not present good results since the number of samples on the dataset is not too much making the data reduction technique not suitable for this dataset. From the four models used K-Nearest Neighbors, Decision Tree Classifier, Logistic Regression, and 1-dimensional Convolutional Neural Network (1D-CNN), the model that outperformed in all the experiments carried out was the 1D-CNN model. Also, the experiment that performs better is the use of data augmentation and not using data reduction. The best score obtained inthe accuracy metric for this combination was 84%.