Contextual Predictors of Protest Behavior on Social Media: A #Ferguson Case Study
Using an original Python program, we extracted data from the 2014 #Ferguson Twitterstorm to explore contextual predictors of digital activism. Through content analysis we identify whether tweets related to police conduct, race relations, and perceptions of justice were influenced by local arrest rates and economic inequality. Instead, results suggest that race and partisanship drove much of the digital protest behavior. Negative perceptions of justice and police conduct largely originated from within Democratic-leaning communities and areas with larger African American populations. We speculate that deeper social grievances surfaced in the Ferguson case, causing the digital conversation to become both racialized and politicized.
Journal of Information Technology & Politics
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