  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.
