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