The spread of disinformation and propagandistic content poses a threat to societal harmony, undermining informed decision-making and trust in reliable sources. Online platforms often serve as breeding grounds for such content, and malicious actors exploit the vulnerabilities of audiences to shape public opinion. Although there have been research efforts aimed at the automatic identification of disinformation and propaganda in social media content, there remain challenges in terms of performance. The ArAIEval shared task aims to further research on these particular issues within the context of the Arabic language. In this paper, we discuss our participation in these shared tasks. We competed in subtasks 1A and 2A, where our submitted system secured positions 9th and 10th, respectively. Our experiments consist of fine-tuning transformer models and using zero- and few-shot learning with GPT-4.

翻译：虚假信息与宣传内容的传播对社会和谐构成威胁，削弱了基于信息的决策能力及对可靠来源的信任。网络平台常成为此类内容的滋生地，恶意行为者利用受众的脆弱性来操纵公众舆论。尽管已有研究致力于自动识别社交媒体内容中的虚假信息和宣传，但性能方面仍存在挑战。ArAIEval共享任务旨在进一步推动阿拉伯语背景下这些特定问题的研究。本文讨论了我们在这些共享任务中的参与情况。我们参与了子任务1A和2A，提交的系统分别获得第9和第10名。我们的实验包括微调Transformer模型，以及使用GPT-4进行零样本和少样本学习。