Dataset of Propaganda Techniques of the State-Sponsored Information Operation of the People's Republic of China
This work addresses the problem of limited research on Chinese propaganda techniques for researchers and analysts, though it is incremental as it builds on existing datasets and methods.
The researchers tackled the lack of fine-grained analysis of propaganda techniques in Chinese Mandarin content by creating a multi-labeled dataset based on state-backed information operations from Twitter, and they applied a fine-tuned BERT model for multi-label text classification, achieving results that could aid in cross-lingual and cross-platform detection.
The digital media, identified as computational propaganda provides a pathway for propaganda to expand its reach without limit. State-backed propaganda aims to shape the audiences' cognition toward entities in favor of a certain political party or authority. Furthermore, it has become part of modern information warfare used in order to gain an advantage over opponents. Most of the current studies focus on using machine learning, quantitative, and qualitative methods to distinguish if a certain piece of information on social media is propaganda. Mainly conducted on English content, but very little research addresses Chinese Mandarin content. From propaganda detection, we want to go one step further to provide more fine-grained information on propaganda techniques that are applied. In this research, we aim to bridge the information gap by providing a multi-labeled propaganda techniques dataset in Mandarin based on a state-backed information operation dataset provided by Twitter. In addition to presenting the dataset, we apply a multi-label text classification using fine-tuned BERT. Potentially this could help future research in detecting state-backed propaganda online especially in a cross-lingual context and cross platforms identity consolidation.