Social media data is effective at predicting crime

Social media generates a tremendous amount of data and police are using it to their advantage

Why it matters: Law breakers have been posting about criminal behavior
on social media for as long as the medium has been around but as recent
research highlights, police don't need people to directly tattle on
themselves to predict where crime will happen.

Law enforcement agencies have established crime prevention techniques
dating back decades. Built on metrics like historical patterns of
events, geographical information and local demographics, this data helps
agencies develop crime prevention tactics that improve patrol
strategies, increase public safety and decrease economic loss.

The problem, however, is that these variables change slowly over time
and do not capture short-term variances associated with real-life crime

Fortunately for law enforcement, they’re able to keep their finger on
the pulse of the public through modern technology such as social media.

As examined in a recent study on the subject, social media platforms
like Twitter and Foursquare generate massive amounts of data that
provide unprecedented opportunities to capture a city’s dynamics. By
feeding seemingly unsuspecting data into an algorithm – for example,
check-ins from Foursquare – researchers were able to use the routine
activity theory to determine that a location with users across a diverse
background is likely to spawn certain types of crime such as theft.

In experiments in Brisbane and New York City, researchers noted that
Area Under Curve (AUC) values improved after factoring in dynamic
features (social media information). Using the Random Forest prediction
model, valued increased four percent for theft, four percent for drug
offenses, 16 percent for assault, two percent for fraud and six percent
for unlawful entry. In New York City, values improved four percent for
theft, two percent for drug offenses, two percent for assault, four
percent for fraud and four percent for unlawful entry.

In both cases, traffic related offenses weren’t impacted by social media intelligence.

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