There are many conflicting theories about the relationship between media reports and public opinion, but few of them are supported by empirical work on large data sets. We use sentiment results on over 260,000 China-related articles in The New York Times to show that events in international relations affect media sentiment which, in turn, affects public opinion. We find that sudden shifts in US-China relations are accompanied by changes in how The New York Times covers China and that the news reporting on China leads public opinion on China by 1 year. Our work illustrates how The New York Times, a prestigious mass media institution, propagates international relation signals to shape American views of the Chinese state and the Chinese people.
Studies on creative industries have explored how producers' properties shape their decisions about pursuing artistic novelty in their products. But how do external shocks, such as new remuneration opportunities, affect artistic novelty? In this paper, I investigate how the launch of monetization programs on online music platforms shapes musical novelty. I examined an original dataset of 12,394 songs on a Chinese music platform that launched its monetization program in 2013. Using computational tools developed in Music Information Retrieval (MIR) to compare song similarity based on their acoustic properties, I employed a novel approach to measure and model the impact of the monetization program on the musical novelty of the songs. The results suggest that while many more songs were uploaded to the platform after the program launch, these new songs are generally less novel than those before. Moreover, the decrease is particularly significant among songs released by indie companies, Pop musicians, or veteran producers. These findings demonstrate that monetization programs will restrain artistic novelty, especially among producers who are more autonomously skillful, unspecialized, or experienced.
How do citizens react to authoritarian responsiveness? To investigate this question, we study how Chinese citizens reacted to a novel government initiative which enabled social media users to publicly post requests for COVID-related medical assistance. To understand the effect of this initiative on public perceptions of government effectiveness, we employ a two-part empirical strategy. First, we conduct a survey experiment in which we directly expose subjects to real help-seeking posts, in which we find that viewing posts did not improve subjects' ratings of government effectiveness, and in some cases worsened them. Second, we analyze over 10,000 real-world Weibo posts to understand the political orientation of the discourse around help-seekers. We find that negative and politically critical posts far outweighed positive and laudatory posts, complementing our survey experiment results. To contextualize our results, we develop a theoretic framework to understand the effects of different types of responsiveness on citizens' political attitudes. We suggest that citizens' negative reactions in this case were primarily influenced by public demands for help, which illuminated existing problems and failures of governance.
One thing that has frequently been overlooked in studies of how a local government responds to its citizens is the text itself. When a petitioner drafts a post, they can pick up words and choose how the demand or question is conveyed. Thus, how a post is composed may also influence local government responsiveness. In this study, we investigated whether a delayed response is due to the way the message is drafted or the actual content by separating content-related and content-agnostic text features. Based on posts retrieved from two websites, we found that the response pattern varies by location, time, and type of queried agency. Our results also indicated that lengthy and low positive sentiment posts generally result in longer waiting times. However, more research is needed to gauge the possibility of a false negative and the meaning of contents extracted from computational tools. In addition, our work scrutinized several methodological issues and sought practical solutions to analyzing big data.
This study examines how China was covered and framed in global media reporting during the early stage of the coronavirus pandemic. Relying on a global multilingual COVID-19 online news narratives dataset, we propose multidimensional indicators to assess cross-country and cross-period variations in media discourses on China throughout the year of 2020. We derive and assess two hypotheses to explore factors accounting for the variations. The ideology-conflict hypothesis argues that the ideology distance from China determines the media attention and framing toward China in terms of COVID-19 reporting, while the crisis-mitigation hypothesis emphasizes that the domestic pandemic situation is associated with media discourses on China. Empirical analysis based on data compiled from various sources finds no evidence for the ideology-conflict hypothesis and moderate support for the crisis-mitigation hypothesis. Changes in the coronavirus situation and policy reactions are associated with changes in media coverage of China and the use of politicized terms over time. We conclude by discussing the implications of using online media data to understand the COVID-19 infodemic and its contribution to the emerging field of computational sociology.
