mlsanityData to 14 Oct 2026

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

arXiv 2410.12791 ↗First posted 16 Oct 2024cs.CLcited by 2

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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.

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

11 of 32 references.

References · 11

  1. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
  2. 2022BERTopic: Neural topic modeling with a class-based TF-IDF procedurecited by 801
  3. 2020Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillationcited by 593
  4. 2020Top2Vec: Distributed Representations of Topicscited by 126
  5. 2020Pre-training is a Hot Topic: Contextualized Document Embeddings Improve Topic Coherencecited by 121
  6. 2020Cross-lingual Contextualized Topic Models with Zero-shot Learningcited by 67
  7. 2017Stability of Topic Modeling via Matrix Factorizationcited by 13
  8. 2017Individuals, Institutions, and Innovation in the Debates of the French Revolutioncited by 10
  9. 2021Event Flow -- How Events Shaped the Flow of the News, 1950-1995cited by 2
  10. 2021News Information Decoupling: An Information Signature of Catastrophes in Legacy News Mediacited by 1
  11. 2021When no news is bad news -- Detection of negative events from news media contentcited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.