UC Santa Barbara

Expression of opinions are communicated on social media platforms, and these records can be analyzed and applied to a variety of spatial research questions. Using geo-tagged social media posts, specifically from the microblogging application Twitter, this research investigates the following research question: Assuming that users’ perceptions of crime can be accurately extracted and analyzed from social media data, to what degree do they match with actual geo-tagged crime incidents in time and space? This research could be serviceable to officers of the law for effective administration of citywide resources with the mission to weaken crime in areas most affecting residents’ perception of their safety. The City and County of San Francisco was chosen for its availability of crime data and Twitter data. Methods applied to explore the posited research questions included preprocessing the Twitter content to remove unnecessary information such as URLs plus retweets. Sentiment analysis in RStudio separated the entire corpus of tweets into emotional categories. Finally, a series of point density and emerging hotspots maps were created to explore the relationships within the data. Comparison between the hotspots maps for the rates of crime and for the rates of tweets reveal different similarities and discrepancies based upon the time range used for crime. Based on the comparison between spatial and temporal patterns of crime and tweets showing fear, the interpretation is that the northeastern areas in San Francisco were more likely to be the site of crime on Fridays around 6 p.m., plausibly larceny. Social media like Twitter may prove as effective indicators or perhaps even predictors of crime in space and time.