How Information Flows from the World to China

Yingdan Lu, Jack Schaefer, Kunwoo Park, Jungseock Joo, Jennifer Pan*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

Abstract

Government censorship—internet shutdowns, blockages, firewalls—impose significant barriers to the transnational flow of information despite the connective power of digital technologies. In this paper, we examine whether and how information flows across borders despite government censorship. We develop a semi-automated system that combines deep learning and human annotation to find co-occurring content across different social media platforms and languages. We use this system to detect co-occurring content between Twitter and Sina Weibo as Covid-19 spread globally, and we conduct in-depth investigations of co-occurring content to identify those that constitute an inflow of information from the global information ecosystem into China. We find that approximately one-fourth of content with relevance for China that gains widespread public attention on Twitter makes its way to Weibo. Unsurprisingly, Chinese state-controlled media and commercialized domestic media play a dominant role in facilitating these inflows of information. However, we find that Weibo users without traditional media or government affiliations are also an important mechanism for transmitting information into China. These results imply that while censorship combined with media control provide substantial leeway for the government to set the agenda, social media provides opportunities for non-institutional actors to influence the information environment. Methodologically, the system we develop offers a new approach for the quantitative analysis of cross-platform and cross-lingual communication.

Original languageEnglish (US)
Pages (from-to)305-327
Number of pages23
JournalInternational Journal of Press/Politics
Volume29
Issue number2
DOIs
StatePublished - Apr 2024

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the National Science Foundation SBE/SMA No. 1831481, No. 1831848 “RIDIR: Collaborative Research: Integrated Communication Database and Computational Tools.” Y. Lu was supported by the Stanford Graduate Fellowship. K. Park was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. 2021R1F1A1062691).

Keywords

  • Covid-19
  • censorship
  • deep learning
  • global communication
  • social media

ASJC Scopus subject areas

  • Communication
  • Sociology and Political Science

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