Information Flows Between Global and Chinese Social Media

• In Event: Information Flows and Authoritarian Control in the Digital Era

Abstract

A large body of research shows that 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 digital 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. We find that less than half of viral tweets appear on Weibo. Among co-occurring content, nearly half likely moved from Weibo to Twitter, while less than one third likely moved from Twitter to Weibo. Information describing the situation on the ground, which was likely useful to global audiences, made its way out of China. Viral tweets condemning the Chinese regime and Chinese people—content likely to elicit negative emotions and possibly confirm existing viewpoints—made its way into China. In contrast to expectation, information flows, both in and out of China, were facilitated by ordinary users more so than by media or online opinion leaders. This paper contributes methodologically to the study of cross-platform and cross-lingual communication and extends our substantive understanding of the implications and limitations of government censorship.

Authors

• Yingdan Lu, Stanford University

• Jack F Schaefer, University of California Los Angeles

• Jungseock Joo, UCLA

• Jennifer Pan, Stanford University