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With the advent of social media data, and in the pursuit of understanding meaning behind those data, text classification continues to grow in importance. Domain expertise is often needed to classify text effectively, but it is unlikely to be found in the tools that provide the means for unsupervised classification. This paper examines a variety of text classification techniques applied to the domain of policing in the U.S. With police and their interaction with the public being in the public spotlight, understanding the messaging police send out via social media offers important insight into how the relationship between the police and the public evolves over time. However, classifying the tweets being sent out via the thousands of police forces across the U.S. is a daunting task. This work tests whether this classification can be automated, and explores the ramifications if it is.

POLICE USE OF SOCIAL MEDIA: COMPARING CLASSIFICATION METHODS