DEEP LEARNING BASED AFAAN OROMO HATE SPEECH DETECTION

This paper examines the viability of deep learning models for Afaan Oromo hate speech recognition. Toward this, we collect and annotate the first and most enormous Afaan Oromo language social media datasets. Variations of profound deep learning models such as CNN, BiLSTMs, LSTM, GRU, and CNN-LSTM are examined to evaluate their viability in identifying Afaan Oromo Hate speeches. The examination result uncovers that the model dependent on CNN and Bi-LSTM outperforms every one of the models on the test dataset with an average F1-score of 87%. Overall, considering the nature of the Afaan Oromo language and the prevalence of hate speech, we believe this study’s finding is promising for future works.