CASM: A DEEP-LEARNING APPROACH FOR IDENTIFYING COLLECTIVE ACTION EVENTS WITH TEXT AND IMAGE DATA FROM SOCIAL MEDIA

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 networb on image data and recurre...

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Bibliographic Details
Published in:Sociological methodology 2019-08, Vol.49 (1), p.1-57
Main Authors: Zhang, Han, Pan, Jennifer
Format: Article
Language:English
Subjects:
Censorship
Collective action
Data
Ethnic conflict
Ethnic relations
Ethnicity
Internet
Mass media images
Memory
Neural networks
Social media
Social movements
Social networks
Symposium on Big Data-Innovations in Computational Social Science
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Summary: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 networb on image data and recurrent neural networb 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.
ISSN:0081-1750
1467-9531
DOI:10.1177/0081175019860244