In this paper, we are exploring mitigating class imbalance in Arabic propaganda detection. Given a multigenre text which could be a news paragraph or a tweet, the objective is to identify the propaganda technique employed in the text along with the exact span(s) where each technique occurs. We approach this task as a sequence tagging task. We utilise AraBERT for sequence classification and implement data augmentation and random truncation methods to mitigate the class imbalance within the dataset. We demonstrate the importance of considering macro-F1 as well as micro-F1 when evaluating classifier performance in this scenario.