  This paper presents the participation of team QUST in Task 8 SemEval 2024. We
first performed data augmentation and cleaning on the dataset to enhance model
training efficiency and accuracy. In the monolingual task, we evaluated
traditional deep-learning methods, multiscale positive-unlabeled framework
(MPU), fine-tuning, adapters and ensemble methods. Then, we selected the
top-performing models based on their accuracy from the monolingual models and
evaluated them in subtasks A and B. The final model construction employed a
stacking ensemble that combined fine-tuning with MPU. Our system achieved 8th
(scored 8th in terms of accuracy, officially ranked 13th) place in the official
test set in multilingual settings of subtask A. We release our system code
at:https://github.com/warmth27/SemEval2024_QUST
