On December 3, 2014, after a grand jury decided not to indict the white police officer in the death of Eric Garner, the social networking platform Twitter was flooded with tweets sharing stances on racial profiling and police brutality. Two hashtags, #CrimingWhileWhite (#cww) and #AliveWhileBlack (#aww), became prominent in these online discussions, serving as a way to highlight and expose the variations individuals may have in their interactions with police dependent on race.
Trending almost simultaneously, the spread of these two hashtags provided a unique opportunity to examine how competing narratives are spread through social media networks – specifically, how black and white narratives of the criminal justice system are expressed, interpreted, shared and re-shared by different or overlapping online communities.
This exploratory research – which used a machine learning approach to content analysis, topic modeling and social network analysis – compared the linguistic and semantic differences between content accompanying the two hashtags. Through conducting both structural and textual analyses of the communities propelling and supporting the spread of these two hashtags, we were able to examine more broadly the actors and networks that played integral roles in making this content more or less visible online, and to highlight potential factors enabling or inhibiting the spread of these black and white experiences of criminal justice, respectively, through social media networks.
Vanessa Kitzie is a Ph.D. student at Rutgers University with interests largely centered around examining the conditions affecting social access to information. Her work employs mixed-methods and examines contexts ranging from social media platforms to libraries.
Debanjan Ghosh is a Ph.D. Candidate at Rutgers University and a Research Associate for DARPA DEFT (Deep Exploration and Filtering of Text) project. His research primarily focuses on Computational Linguistics problems, such as automatically inferring meaning of words and how composition of word meaning assists in perceiving phrases, sentences, and discourses. He is also interested in Computational models of social meaning in text, such as argument mining in online interactions, content analysis of political debates, and identifying sarcasm in social media. Before joining Rutgers, Debanjan was with Thomson Reuters R&D at New York.