The use of propaganda has spiked on mainstream and social media, aiming to
manipulate or mislead users. While efforts to automatically detect propaganda
techniques in textual, visual, or multimodal content have increased, most of
them primarily focus on English content. The majority of the recent initiatives
targeting medium to low-resource languages produced relatively small annotated
datasets, with a skewed distribution, posing challenges for the development of
sophisticated propaganda detection models. To address this challenge, we
carefully develop the largest propaganda dataset to date, ArPro, comprised of
8K paragraphs from newspaper articles, labeled at the text span level following
a taxonomy of 23 propagandistic techniques. Furthermore, our work offers the
first attempt to understand the performance of large language models (LLMs),
using GPT-4, for fine-grained propaganda detection from text. Results showed
that GPT-4's performance degrades as the task moves from simply classifying a
paragraph as propagandistic or not, to the fine-grained task of detecting
propaganda techniques and their manifestation in text. Compared to models
fine-tuned on the dataset for propaganda detection at different classification
granularities, GPT-4 is still far behind. Finally, we evaluate GPT-4 on a
dataset consisting of six other languages for span detection, and results
suggest that the model struggles with the task across languages. Our dataset
and resources will be released to the community.

使用 GPT-4 大型语言模型对包含 6 种其他语言的传媒行文进行细粒度的宣传技术检测，结果显示该模型在不同语种的跨度检测任务中遇到困难，相比于用于宣传检测的不同分类层次的模型微调后，GPT-4 仍然远远落后。