Nexus at ArAIEval Shared Task: Fine-Tuning Arabic Language Models for
  Propaganda and Disinformation Detection
Yunze Xiao Firoj Alam
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.