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

TL;DR

This paper introduces KeyNMF, a novel topic modelling approach using transformer-based embeddings, to analyze information dynamics in Chinese diaspora media and assess PRC influence on European elections.

Contribution

The paper presents KeyNMF, a new static and dynamic topic modelling method that outperforms existing models on Chinese datasets and enables large-scale analysis of media narratives.

Findings

KeyNMF is effective in capturing topical changes over time.

The pipeline reveals PRC narratives in diaspora media before 2024 elections.

Method provides a scalable tool for studying media influence and information flow.

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.