Introduction to the Special Issue on Images as Data

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.

Voters evaluate politicians not just by what they say, but also how they say it, via facial displays of emotions and vocal pitch. Candidate characteristics can shape how leaders use—and how voters react to—nonverbal cues. Drawing on role congruity expectations, we study how the use of and reactions to facial, vocal, and textual communication in political debates varies by candidate gender. Relying on full-length videos of four German federal election debates (2005–2017) and a minor party debate, we use video, audio, and text data to measure candidate facial displays of emotion, vocal pitch, and speech sentiment. Consistent with our expectations, Angela Merkel expresses less anger than her male opponents, but she is just as emotive in other respects. Combining these measures of emotional expression with continuous responses recorded by live audiences, we find that voters punish Merkel for anger displays and reward her happiness and general emotional displays.