The search found 123 results in 0.027 seconds.
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).
This article explores the relationship between hardships and protest in the world-system. Despite the history of discussion of anti-systemic protest, there has been little work that differentiates world-systems contributions to social movement research from others who examine social movements. We contribute to a theory of anti-systemic protest by re-introducing hardships as a crucial element that defines inequalities in the world-system; one consistent source of those hardships are austerity policies imposed in response to debt negotiations.
We argue word embedding models are a useful tool for the study of culture using a historical analysis of shared understandings of social class as an empirical case. Word embeddings represent semantic relations between words as relationships between vectors in a high-dimensional space, specifying a relational model of meaning consistent with contemporary theories of culture.