Identification of hateful amharic language memes on facebook using deep learning algorithms

Hate speech has been disseminated more frequently on social media sites like Facebook in recent years. On Facebook, hate speech can proliferate through text, image, or video. We suggested a deep learning approach to identify offensive memes posted on Facebook in case of Amharic language'. The research process commenced by manually gathering memes posted by Facebook users. Next came textual data extraction, annotation, preprocessing, splitting, feature extraction, model development and assessment Amharic OCRs were employed to extract textual data. Character normalization, stop word removal, and unnecessary character removal make up the text-preprocessing step. Using Stratified KFold the textual dataset is split into the train set (80 %), the validation set (10 %) and the test set (10 %). Vectors are created from the preprocessed texts using the Bog of words (BOW), TFIDF and word embeddings. Following that, the vectors are fed into Machine learning algorithms: NB, DT, RF, KNN, LSVM and LR, and deep learning models that are based on Dense, BiGRU, and BiLSTM algorithms. The model with the optimal parameters is chosen after numerous experiments. With an accuracy rate of 94 %, the BiLSTM + Dense model, the suggested technique identified nasty meme posts on Facebook written in Amharic.