RT Article
T1 Crime sensing with Big Data: the affordances and limitations of using open-source communications to estimate crime patterns
JF The British journal of criminology
VO 57
IS 2
SP 320
OP 240
A1 Williams, Matthew L. 1976-
A2 Burnap, Pete
A2 Sloan, Luke
LA English
YR 2017
UL https://krimdok.uni-tuebingen.de/Record/1565551702
AB This paper critically examines the affordances and limitations of big data for the study of crime and disorder. We hypothesize that disorder-related posts on Twitter are associated with actual police crime rates. Our results provide evidence that naturally occurring social media data may provide an alternative information source on the crime problem. This paper adds to the emerging field of computational criminology and big data in four ways: (1) it estimates the utility of social media data to explain variance in offline crime patterns; (2) it provides the first evidence of the estimation offline crime patterns using a measure of broken windows found in the textual content of social media communications; (3) it tests if the bias present in offline perceptions of disorder is present in online communications; and (4) it takes the results of experiments to critically engage with debates on big data and crime prediction.
K1 Social media
K1 Computational criminology
K1 Predictive policing
K1 Big data
K1 Broken windows
K1 Open-source communications
K1 Datensammlung
K1 Soziale Medien
K1 Informatik-basierte Kriminologie
DO 10.1093/bjc/azw031