Social media posts are full of data that, when made accessible to governments, can make interventions quicker, more effective, and more representative. From pictures of emergency conditions to posts about crimes in progress, users constantly inundate Facebook, Instagram, Twitter, and other platforms with posts that may contain rich and timely information about events relevant to public safety. Using social media mining that leverages advances in natural language processing and machine learning to pull useful data from text and images, cities can transform these social posts into data points ripe for analysis. Policing is one area that seems an obvious fit for social mining initiatives. Social monitoring has great potential to make policing more proactive, targeting incidents before they escalate into tragedies. By deploying software that can sift through mountains of posts and identify relevant keywords, governments can track posts indicative of danger or criminal activity and intervene.
These social monitoring efforts have become increasingly commonplace in police departments. A report by the Brennan Center for Justice at the NYU School of Law showed that nearly all large cities, and many smaller ones, have made significant investments in social media monitoring tools. A 2016 survey by the International Association of Chiefs of Police and Urban Institute revealed that 76 perce