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In this paper, we highlight our approach for the "Arabic AI Tasks Evaluation(ArAiEval) Shared Task 2023". We present our approaches for task 1-A and task2-A of the shared task which focus on persuasion technique detection anddisinformation detection respectively. Detection of persuasion techniques anddisinformation has become imperative to avoid distortion of authenticinformation. The tasks use multigenre snippets of tweets and news articles forthe given binary classification problem. We experiment with severaltransformer-based models that were pre-trained on the Arabic language. Wefine-tune these state-of-the-art models on the provided dataset. Ensembling isemployed to enhance the performance of the systems. We achieved a microF1-score of 0.742 on task 1-A (8th rank on the leaderboard) and 0.901 on task2-A (7th rank on the leaderboard) respectively.

Mavericks at ArAIEval Shared Task: Towards a Safer Digital Space -- Transformer Ensemble Models Tackling Deception and Persuasion