Özet:

As a result of people communicating online, especially on social media platforms, the amount of user-generated content on the internet has increased. Due to the anonymous nature of these platforms, users can share content containing offensive language and hate speech. Such undesirable content can have negative effects on both individuals and societies. Therefore, it is important to detect and filter content that contains offensive language and hate speech. Detecting such content manually is challenging, which is why there is a need for automated methods. In recent years, there has been an increase in academic research on the detection of online offensive language and hate speech. Promising results have been achieved in the automatic detection of offensive language and hate speech in English using transfer learning models such as BERT. However, the number of studies on automatic detection of offensive language and hate speech in languages with limited resources such as Turkish is quite limited. This study presents the results of efforts to develop an automatic detection system for offensive language and hate speech in the Turkish language. Firstly, an automatic labeling method was proposed to create a Turkish dataset, and using this method, a Turkish dataset for hate speech and offensive language was created. Experiments were conducted using various variants of the BERT model, which is considered state-of-the-art in natural language processing, along with various Turkish datasets related to offensive language and hate speech. Through these experiments, the XLM-RoBERTa model, which achieved the best performance, underwent hyperparameter optimization. Subsequently, using the most comprehensive datasets available, the final Turkish automatic detection system for offensive language and hate speech was developed. The developed Turkish automatic detection model for offensive language and hate speech was compared with other studies using the same test dataset.

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