This model is a multilingual hate speech classifier based on the XLM-R architecture. It is trained to detect hate speech in English, Italian, and Slovene. The model leverages multilingual datasets and incorporates techniques to learn from disagreement among annotators, making it robust in understanding and identifying nuanced hate speech across different languages. It has been developed as part of my Master's thesis and the training methodology follows the approach outlined by Kralj Novak et al. (2022) in their paper
"Handling Disagreement in Hate Speech Modelling".
The model is trained on a multilingual dataset consisting of Twitter and YouTube comments in EN, IT and SL. The dataset consists of diamond standard data, i.e. an alternative to the gold standard that takes into account the perspectives of multiple annotators. This is particularly useful for highly subjective tasks such as annotating hate speech, where the idea of a single truth may be debatable.
These metrics are computed for each language separately, as well as across the entire multilingual dataset. Krippendorff's Alpha was used to measure both the disagreement between the annotators themselves and
between the annotators and the model.
The primary use case for this model is to automatically detect and moderate hate speech on social media platforms, online forums, and other digital content platforms. This can help in reducing the spread of harmful content and maintaining a safe online environment.
