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Context is Key(NMF): Modelling Topical Information Dynamics in Chinese Diaspora Media

Ross Deans Kristensen-McLachlan, Rebecca M. M. Hicke, Márton Kardos, Mette Thunø

Abstract

Does the People's Republic of China (PRC) interfere with European elections through ethnic Chinese diaspora media? This question forms the basis of an ongoing research project exploring how PRC narratives about European elections are represented in Chinese diaspora media, and thus the objectives of PRC news media manipulation. In order to study diaspora media efficiently and at scale, it is necessary to use techniques derived from quantitative text analysis, such as topic modelling. In this paper, we present a pipeline for studying information dynamics in Chinese media. Firstly, we present KeyNMF, a new approach to static and dynamic topic modelling using transformer-based contextual embedding models. We provide benchmark evaluations to demonstrate that our approach is competitive on a number of Chinese datasets and metrics. Secondly, we integrate KeyNMF with existing methods for describing information dynamics in complex systems. We apply this pipeline to data from five news sites, focusing on the period of time leading up to the 2024 European parliamentary elections. Our methods and results demonstrate the effectiveness of KeyNMF for studying information dynamics in Chinese media and lay groundwork for further work addressing the broader research questions.

Context is Key(NMF): Modelling Topical Information Dynamics in Chinese Diaspora Media

Abstract

Does the People's Republic of China (PRC) interfere with European elections through ethnic Chinese diaspora media? This question forms the basis of an ongoing research project exploring how PRC narratives about European elections are represented in Chinese diaspora media, and thus the objectives of PRC news media manipulation. In order to study diaspora media efficiently and at scale, it is necessary to use techniques derived from quantitative text analysis, such as topic modelling. In this paper, we present a pipeline for studying information dynamics in Chinese media. Firstly, we present KeyNMF, a new approach to static and dynamic topic modelling using transformer-based contextual embedding models. We provide benchmark evaluations to demonstrate that our approach is competitive on a number of Chinese datasets and metrics. Secondly, we integrate KeyNMF with existing methods for describing information dynamics in complex systems. We apply this pipeline to data from five news sites, focusing on the period of time leading up to the 2024 European parliamentary elections. Our methods and results demonstrate the effectiveness of KeyNMF for studying information dynamics in Chinese media and lay groundwork for further work addressing the broader research questions.

Paper Structure

This paper contains 19 sections, 9 figures, 2 tables.

Figures (9)

  • Figure 1: Sensitivity of KeyNMF to the choice of $N$ keywords on multiple metrics and news sources.
  • Figure 2: The total and unique number of articles collected for each news site.
  • Figure 3: The number of new articles collected at each time point for each source. An article is 'new' if it did not appear in the collected set of articles from the previous time point.
  • Figure 4: The novelty and resonance plots for each news site from KeyNMF with ten topics. The three shaded areas represent Xi Jinping's European tour (May 5-10, 2024), Putin's state visit to China (May 16-17, 2024), and the EU parliamentary elections (June 6-9, 2024). Note that the y-axis ranges differ for each chart.
  • Figure 5: The novelty and resonance plots for each news site from KeyNMF with 25 topics. The three shaded areas represent Xi Jinping's European tour (May 5-10, 2024), Putin's state visit to China (May 16-17, 2024), and the EU parliamentary elections (June 6-9, 2024). Note that the y-axis ranges differ for each chart.
  • ...and 4 more figures