Exploring the Dataset Landscape for Automated Propaganda Detection: A Data-Centric Insight

The increasing spread of propaganda in digital media has intensified research efforts toward the development of automated detection systems. Central to this task is the availability and quality of annotated datasets, which directly impact model performance, generalizability, and real-world applicability. In this paper, we present a data-centric insight into the current landscape of datasets used for automated propaganda detection. We analyze a representative set of publicly available corpora with respect to key factors such as annotation schemes, label granularity, domain coverage, linguistic diversity, and class balance. This work aims to guide researchers toward more robust, inclusive, and scalable approaches to propaganda detection by emphasizing the foundational role of data quality and structure.