  The use of propagandistic techniques in online contents has increased in
recent years aiming to manipulate online audiences. Efforts to automatically
detect and debunk such content have been made addressing various modeling
scenarios. These include determining whether the content (text, image, or
multimodal) (i) is propagandistic, (ii) employs one or more propagandistic
techniques, and (iii) includes techniques with identifiable spans. Significant
research efforts have been devoted to the first two scenarios compared to the
latter. Therefore, in this study, we focus on the task of detecting
propagandistic textual spans. Specifically, we investigate whether large
language models (LLMs), such as GPT-4, can effectively perform the task.
Moreover, we study the potential of employing the model to collect more
cost-effective annotations. Our experiments use a large-scale in-house dataset
consisting of annotations from human annotators with varying expertise levels.
The results suggest that providing more information to the model as prompts
improves its performance compared to human annotations. Moreover, our work is
the first to show the potential of utilizing LLMs to develop annotated datasets
for this specific task, prompting it with annotations from human annotators
with limited expertise. We plan to make the collected span-level labels from
multiple annotators, including GPT-4, available for the community.
