title: Context is Key(NMF): Modelling Topical Information Dynamics in Chinese
    Diaspora Media
summary: 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_authors._id: 671219417b8d16aeed706386
paper_authors.name: Ross Deans Kristensen-McLachlan
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-10-21T12:27:23.866Z
paper_authors._id: 62b5cade593a2c49da706c8c
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paper_authors.fullname: Ross Kristensen-McLachlan
paper_authors.type: user
paper_authors.user: rdkm89
paper_authors._id: 671219417b8d16aeed706387
paper_authors.name: Rebecca M. M. Hicke
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-10-21T12:25:50.898Z
paper_authors._id: 6580701a1fb6cfa0b46c61ac
paper_authors.avatarUrl: /avatars/359b9aa336eed1504ee57969fbe3ec52.svg
paper_authors.fullname: Rebecca M. M. Hicke
paper_authors.type: user
paper_authors.user: rmmhicke
paper_authors._id: 671219417b8d16aeed706388
paper_authors.name: Márton Kardos
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-10-21T07:48:09.117Z
paper_authors._id: 62696cd3d1ac0cde59280dcf
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1656940964610-62696cd3d1ac0cde59280dcf.jpeg
paper_authors.fullname: Márton Kardos
paper_authors.type: user
paper_authors.user: kardosdrur
paper_authors._id: 671219417b8d16aeed706389
paper_authors.name: Mette Thunø
paper_title: Context is Key(NMF): Modelling Topical Information Dynamics in Chinese
    Diaspora Media
paper_summary: 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_ai_summary: KeyNMF, a new approach to topic modeling using transformer-based embeddings, is introduced and evaluated for studying information dynamics in Chinese media, specifically in the context of PRC narratives related to European elections.
paper_ai_keywords: KeyNMF topic modelling transformer-based contextual embedding models information dynamics benchmark evaluations European parliamentary elections