As part of our study, we worked on three tasks: stance detection, sarcasm detection and senti- ment analysis using fine-tuning techniques on BERT-based models. Fine-tuning parameters were carefully adjusted over multiple iterations to maximize model performance. The three tasks are essential in the field of natural lan- guage processing (NLP) and present unique challenges. Stance detection is a critical task aimed at identifying a writer’s stances or view- points in relation to a topic. Sarcasm detection seeks to spot sarcastic expressions, while senti- ment analysis determines the attitude expressed in a text. After numerous experiments, we iden- tified Arabert-twitter as the model offering the best performance for all three tasks. In particu- lar, it achieves a macro F-score of 78.08% for stance detection, a macro F1-score of 59.51% for sarcasm detection and a macro F1-score of 64.57% for sentiment detection. . Our source code is available at https:// github.com/MezghaniAmal/Mawqif