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This study demonstrates how localization and homogenization can co-occur in different aspects of smartphone usage. Smartphones afford individualization of media behavior: users can begin, end, or switch between countless tasks anytime, but this individualization is shaped by shared environments such that smartphone usage may be similar among those who share such environments but contain differences, or localization, across environments or regions. Yet for all users, smartphone screen interactions are bounded and guided by nearly identical smartphone interfaces, suggesting that smartphone usage may be similar or homogenized across all individuals regardless of environment. We study homogenization and localization by comparing the temporal, visual, and experiential composition of screen activity among individuals in three dissimilar media environments—the United States, China, and Myanmar—using one week of screenshot data captured passively every 5 s by the novel Screenomics framework. We find that overall usage levels are consistently dissimilar across media environments, while metrics that depend more on moment-level decisions and user-interface design do not vary significantly across media environments. These results suggest that quantitative research on homogenization and localization should analyze behavior driven by user interfaces and by contextually determined parameters, respectively. 

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.

 

As audiences have moved to digital media, so too have governments around the world. While previous research has focused on how authoritarian regimes employ strategies such as the use of fabricated accounts and content to boost their reach, this paper reveals two di!ferent tactics the Chinese government uses on Douyin, the Chinese version of the video-sharing platform TikTok, to compete for audience attention. We use a multi-modal approach that combines analysis of video, text, and meta-data to examine a novel dataset of Douyin videos. We find that a large share of trending videos are produced by accounts affiliated with the Chinese government. These videos contain visual characteristics designed to maximize attention such as high levels of brightness and entropy and very short duration, and are more visually similar to content produced by celebrities and ordinary users than to content from non-official media accounts. We also find that the majority of videos produced by regime-affiliated accounts do not fit traditional definitions of propaganda but rather contain stories and topics unrelated to any aspect of the government, the Chinese Communist Party, policies, or politics. 

The proliferation of social media and digital technologies has made it necessary for governments to expand their focus beyond propaganda content in order to disseminate propaganda effectively. We identify a strategy of using clickbait to increase the visibility of political propaganda. We show that such a strategy is used across China by combining ethnography with a computational analysis of a novel dataset of the titles of 197,303 propaganda posts made by 213 Chinese city-level governments on WeChat. We find that Chinese propagandists face intense pressures to demonstrate their effectiveness on social media because their work is heavily quantified–measured, analyzed, and ranked–with metrics such as views and likes. Propagandists use both clickbait and non-propaganda content (e.g., lifestyle tips) to capture clicks, but rely more heavily on clickbait because it does not decrease space available for political propaganda. Government propagandists use clickbait at a rate commensurate with commercial and celebrity social media accounts. The use of clickbait is associated with more views and likes, as well as greater reach of government propaganda outlets and messages. These results reveal how the advertising-based business model and affordances of social media influence political propaganda and how government strategies to control information are moving beyond censorship, propaganda, and disinformation. 

Protest event analysis is an important method for the study of collective action and social movements and typically draws on traditional media reports as the data source. We introduce collective action from social media (CASM)—a system that uses convolutional neural networks on image data and recurrent neural networks with long short-term memory on text data in a two-stage classifier to identify social media posts about offline collective action. We implement CASM on Chinese social media data and identify more than 100,000 collective action events from 2010 to 2017 (CASM-China). We evaluate the performance of CASM through cross-validation, out-of-sample validation, and comparisons with other protest data sets. We assess the effect of online censorship and find it does not substantially limit our identification of events. Compared to other protest data sets, CASM-China identifies relatively more rural, land-related protests and relatively few collective action events related to ethnic and religious conflict.