Team Curie at HSD -2 L ang 2024: Hate Speech Detection in T urkish and A rabic Tweets using BERT -based models (2024)

Abstract: Team Curie at HSD-2Lang 2024: Team Curie at HSD-2Lang 2024: Hate Speech Detection in Turkish and Arabic Tweets using BERT-based models This paper has presented our methodologies and findings in tackling hate speech detection in Turkish and Arabic tweets as part of the HSD-2Lang 2024 contest. Through innovative approaches and the fine-tuning of BERT-based models, we have achieved notable F1 scores, demonstrating the potential of our models in addressing the linguistic challenges inherent in Turkish and Arabic languages. The ablation study for Subtask A provided valuable insights into the impact of preprocessing and data balancing on model performance, guiding future enhancements. Our work contributes to the broader goal of improving online content moderation and safety, with future research directions including the expansion to more languages and the integration of multi-modal data and explainable AI techniques.

## introduction social media platforms like twitter, facebook, and youtube have become pivotal for expressing opinions and sharing information. however, hate speech-targeting ethnic, religious, gender, or other societal groups-poses a significant challenge to social harmony. the need for efficient detection mechanisms is amplified by the global reach of such content, yet languages like turkish and arabic present specific hurdles due to their intricate linguistic features and scarce annotated datasets @xcite . the hate speech detection in turkish and arabic tweets (hsd-2lang) shared task foot_1 , part of case @ eacl 2024 @xcite , builds on the siu2023-nst competition's groundwork in turkish to include arabic. this expansion highlights the need for language-specific solutions capable of accurately identifying hate speech in varied contexts. our contribution to subtask a and subtask b of this shared task underscores our commitment to advancing hate speech detection in turkish and arabic. through our methodologies, we aim to contribute to the development of safer digital environments. ## related work the detection of hate speech, especially in linguistically complex languages like turkish, has garnered significant attention in natural language processing research. @xcite presented a berturk-based approach at lrec 2022, highlighting the effectiveness of context-specific training with domain-specific datasets, achieving notable accuracies on the istanbul convention and refugees datasets. ## system architecture and training this section details the system architecture and training processes for each distinct subtask. ## experimental results in this section, we summarize the performance of our models for each subtask. our models were evaluated on a test dataset provided by the shared task organizers on kaggle 67 . ## ablation study for subtask a in our ablation study for subtask a, we systematically evaluated the impact of various preprocessing steps and data balancing techniques on the model's f1 score. this involved selectively omitting individual preprocessing steps-such as newline and extra space removal, url removal, emoji conversion to text, mention and symbol removal, and hashtag processing-to assess their contribution to the model's overall performance. additionally, we explored the effects of label and topic balancing, both