Özet:

Hate speech is the general name for speech that expresses hatred towards a person or a group or encourages violence. These discourses have recently increased uncontrollably in digital environments. Written hate speech, especially on social media such as Twitter, has reached dangerous dimensions for both individuals and communities. In order to prevent the spread of hate speech in digital environments easily and quickly, systems that can automatically detect these speeches are needed. In our study, artificial intelligence models that can automatically detect 'offensive' speech, which is one of the most common hate speeches, are discussed. In our study, in which deep and shallow machine learning methods are used comparatively, the discourses in Turkish tweets can be divided into 2 categories as offensive or not. In the models we developed using a dataset with an imbalance of approximately 75%-25%, successful results are obtained with a rate of 0.85 on the accuracy and 0.74 on the f-score. The classification results obtained from shallow models trained using term frequency-inverse document frequency (tf-idf) vectors of tweets in the dataset and deep models trained using word embeddings are presented comparatively in this study. Experimental studies have shown that the hate speech detection model developed using Bidirectional Long Short-Term Memory (BiLSTM) technique produces more successful results than shallow methods and some other deep learning methods.

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