mlsanityData to 14 Oct 2026

Dataset of Propaganda Techniques of the State-Sponsored Information Operation of the People's Republic of China

arXiv 2106.07544 ↗First posted 14 Jun 2021cs.SI cs.AI cs.LGcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 5 of its 31 references that are in the map. First posted on arXiv 14 Jun 2021.

Builds on

5 of 31 references.

References · 5

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  3. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  4. 2019Fine-Grained Analysis of Propaganda in News Articlescited by 133
  5. 2020A Survey on Computational Propaganda Detectioncited by 70

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Citing papers per year: 2022: 1; 2023: 0; 2024: 0; 2025: 1.

2 papers.