Abstract: The rising level of automation has increasingly attracted scholar’s attention. On the other hand, there are many studies of the consequences of social movements, but relatively fewer studies focus on their economic consequences, and even fewer studies have examined their consequences on automation. This article bridges the gap between the two literatures by hypothesizing that a rising number of labor protests will lead to a higher level of automation. We argue that political economy factors influence the adoption of more automation.
Abstract: 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.
There are three great invited commentaries to our article by Zachary C. Steinert-Threlkeld, Swen Hutter, and Pamela Oliver. Read them and our response here.
Abstract: 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.
Abstract: Data from Twitter have been employed in prior research to study the impacts of events. Conventionally, researchers use keyword-based samples of tweets to create a panel of Twitter users who mention event-related keywords during and aer an event. However, the keyword-based sampling is limited in its objectivity dimension of data and information quality. First, the technique suers from selection bias since users who discuss an event are already more likely to discuss event-related topics beforehand.
How does physically witnessing a protest in a democratic society affect citizens in authoritarian societies? Existing data are unable to answer this question because of their difficulty in capturing witnesses, constructing meaningful comparison …