Uncovering Wireless Blackspots using Twitter Data

Abstract

Blackspots are areas of poor signal coverage or service delivery that leads to customer complaints and loss in business revenue. Understanding their spatial–temporal patterns at a high resolution is important for interventions. Conventional methods such as customer helplines, drive-by testing, and network analysis tools often lack the real-time capability and spatial accuracy required. The potential of utilising geo-tagged Twitter data to uncover blackspots is investigated. Lexicon and machine-learning natural language processing techniques are applied to over 1.4 million Tweets in London to uncover blackspots for both pre-4G (2012) and post-4G (2016) roll out.

It was found that long-term poor signal complaints make up the majority of complaints (86%) pre-4G roll out, but short-term network failure was responsible for most complaints (66%) post-4G roll out.

Citation information

Guo, W.; Zhang, J.: 'Uncovering wireless blackspots using Twitter data', Electronics Letters, 2017, 53, (12), p. 814-816, DOI: 10.1049

Additional information

Guo W, Zhang J

Turing affiliated authors

Dr Weisi Guo

Honorary Professor at University of Warwick & Professor of Human Machine Intelligence at Cranfield University