The Bank of England mines social media in bid to predict economic events ahead of time

In a new blog three members of the Bank's advanced analytics division, and one from its IT build and maintain division, said "social media provides a rich vein from which to mine information, and text data more generally can reveal trends in people’s opinions and sentiment on specific topics or events".

They said that central banks are "adopting" text mining "tools and techniques to address a wide range of potential applications across central banking, and build more agile and wide-ranging data analysis capabilities for the future".

The blog revealed that the Bank set up a group to see whether data from Twitter posts could help predict a bank run. The group built an "experimental system" that allowed it to filter, collect and analyse 'tweets' in the run up to, day of, and immediate aftermath of the Scottish independence referendum in September 2014. It used the system to see if the Twitter data indicated that there was going to be a run on the banks, it said.

"The group pioneered an innovative approach using social media as a unique lens through which to observe developments in real-time," the blog said. "We analysed traffic on the Twitter micro-blogging platform in the run-up to the vote, searching for tweets including terms or phrases that may have indicated depositors preparing to withdraw money from Scottish financial institutions. The aim was to monitor the volume of traffic matching these search terms, and provide an early warning system in the event of a spike."

The Bank said that it had set "relatively narrow" filters to capture specific tweets and avoid having to try to wade through "noise" that would potentially be irrelevant.

"We chose to search for a range of terms indicating financial stability concerns or referencing specific institutions, together with some link to the referendum or Scotland itself," it said. "Clearly, we could have widened the search criteria and gathered a substantially larger amount of data on a wider range of topics. This would almost certainly have captured more relevant information, but also a much higher volume of noise, and potentially a series of false alarms requiring manual intervention to investigate."

"Instead, we recognised that there was no need to capture every single relevant tweet, and felt confident that any material change would still be visible swiftly via our filters," it said.

The Bank said that the monitoring exercise was a valuable one which has helped it to build "capabilities and knowledge to serve as a foundation for future projects". It said there are "broader opportunities to be explored" and that "greater volumes of Twitter data can reveal broader patterns of human activity and their impact on the economy".

Other online communication platforms, like blogs, can also be mined to help predict economic events, it said.