The system could also identify Twitter rumors about where riots were likely to take place, as well real-time information about areas where people were gathering.
The researchers used a series of machine-learning algorithms to analyze each of the tweets from the dataset of 1.6 million tweets, taking into account a number of key features such as the time they were posted, the location where they were posted and the content of the tweet itself. The algorithms detected incidents quicker than police sources in all but two of the events reported.
The study comes just days after the chief constable of West Midlands Police said that, thanks to budget cuts, police would face "real challenges" tackling a repeat of the 2011 riots. Could Twitter-monitoring systems help pick up the shortfall if a similar event was to happen again?
The report's authors think so. Co-author of the study Dr Pete Burnap, from Cardiff University's School of Computer Science and Informatics, said: "We have previously used machine-learning and natural language processing on Twitter data to better understand online deviance, such as the spread of antagonistic narratives and cyber hate.
"In this research we show that online social media are becoming the go-to place to report observations of everyday occurrences -- including social disorder and terrestrial criminal activity. We will never replace traditional policing resource on the ground but we have demonstrated that this research could augment existing intelligence gathering and draw on new technologies to support more established policing methods."