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

arXiv: https://arxiv.org/abs/2410.12791 · HTML: https://mlsanity.com/paper/2410.12791/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 32 references that are in the map.
- First posted on arXiv 16 Oct 2024.

## Builds on (most cited first, 11 of 11)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://mlsanity.com/paper/2203.05794/) (2022)
- [Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation](https://mlsanity.com/paper/2004.09813/) (2020)
- [Top2Vec: Distributed Representations of Topics](https://mlsanity.com/paper/2008.09470/) (2020)
- [Pre-training is a Hot Topic: Contextualized Document Embeddings Improve Topic Coherence](https://mlsanity.com/paper/2004.03974/) (2020)
- [Cross-lingual Contextualized Topic Models with Zero-shot Learning](https://mlsanity.com/paper/2004.07737/) (2020)
- [Stability of Topic Modeling via Matrix Factorization](https://mlsanity.com/paper/1702.07186/) (2017)
- [Individuals, Institutions, and Innovation in the Debates of the French Revolution](https://mlsanity.com/paper/1710.06867/) (2017)
- [Event Flow -- How Events Shaped the Flow of the News, 1950-1995](https://mlsanity.com/paper/2109.08589/) (2021)
- [News Information Decoupling: An Information Signature of Catastrophes in Legacy News Media](https://mlsanity.com/paper/2101.02956/) (2021)
- [When no news is bad news -- Detection of negative events from news media content](https://mlsanity.com/paper/2102.06505/) (2021)

## Built on by (most cited first, 2 of 2)

- [topicwizard -- a Modern, Model-agnostic Framework for Topic Model Visualization and Interpretation](https://mlsanity.com/paper/2505.13034/) (2025)
- [More Yap Less Meaning: Uncovering Self-Improvement Behavior in SLMs](https://mlsanity.com/paper/2606.08471/) (2026)

## Abstract (arXiv)

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.