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.

中文翻译：

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中华人民共和国国家信息化运作宣传技术数据集

被识别为计算宣传的数字媒体为宣传无限扩大其影响范围提供了途径。国家支持的宣传旨在塑造受众对实体的认知，从而支持某个政党或权威。此外，它已成为现代信息战的一部分，用于获得对对手的优势。目前的大多数研究都集中在使用机器学习、定量和定性方法来区分社交媒体上的某条信息是否是宣传。主要针对英语内容进行，但很少有研究针对中文普通话内容。从宣传检测来看，我们希望更进一步，提供有关所应用宣传技术的更细粒度的信息。在这项研究中，我们的目标是通过基于 Twitter 提供的国家支持的信息操作数据集提供一个多标签的普通话宣传技术数据集来弥合信息差距。除了呈现数据集之外，我们还使用微调的 BERT 应用了多标签文本分类。这可能有助于未来研究检测国家支持的在线宣传，尤其是在跨语言环境和跨平台身份整合中。