Jilan, Tahsin Zaman2026-08-202026-08-202025-01-01T. Z. Jilan, "The Effectiveness of Different Deep Learning Models in Detecting Hate Speech on Social Media," 2025 International Conference on Electrical, Computer and Communication Engineering (ECCE), Chittagong, Bangladesh, 2025, pp. 1-6, doi: 10.1109/ECCE64574.2025.11013825.97983503575092-s2.0-105007835299https://hdl.handle.net/10361/29395Recent changes in social media made it harder to control the propagation of hate speech. One potential solution can be use of deep learning models for automated hate speech recognition. In this work, we evaluate how well different deep learning models classify hate speech on social networks. In our experiments, we use a dataset of social media posts with and without hate speech.We examine the results of a number of different models, including attention-based models, convolutional neural networks (CNNs), and long short-term memory (LSTM). We also examine the effects of additional variables, such as the amount of training data and the use of pre-trained word embeddings, on the performance of these models. Our results demonstrate that attention-based models perform better than CNN and LSTM algorithms in identifying hate speech. To sum up, our research offers valuable perspectives on enhancing deep learning models for the identification of hate speech.6 Pagesen-USDeep learningSocial networking (online)Computational modelingHate speechTraining dataSpeech recognitionTokenizationData modelsConvolutional neural networksLong short term memoryConvolutional neural networks (CNNs)Long short-term memory (LSTM)Deep learning (Machine learning).Conference Proceeding10.1109/ECCE64574.2025.11013825