Noise Annoyance in the UAE: A Twitter Case Study via a Data-Mining Approach

Andrew Peplow, Justin Thomas and Aamna AlShehhi
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Andrew Peplow: Division of Engineering Acoustics, Department of Construction Sciences, Lund University, 221 00 Lund, Sweden
Justin Thomas: College of Natural and Health Sciences, Zayed University, Abu Dhabi 144534, United Arab Emirates
Aamna AlShehhi: Electrical and Computer Engineering, Khalifa University, Abu Dhabi 127788, United Arab Emirates

IJERPH, 2021, vol. 18, issue 4, 1-9

Abstract: Noise pollution is a growing global public health concern. Among other issues, it has been linked with sleep disturbance, hearing functionality, increased blood pressure and heart disease. Individuals are increasingly using social media to express complaints and concerns about problematic noise sources. This behavior—using social media to post noise-related concerns—might help us better identify troublesome noise pollution hotspots, thereby enabling us to take corrective action. The present work is a concept case study exploring the use of social media data as a means of identifying and monitoring noise annoyance across the United Arab Emirates (UAE). We explored an extract of Twitter data for the UAE, comprising over eight million messages (tweets) sent during 2015. We employed a search algorithm to identify tweets concerned with noise annoyance and, where possible, we also extracted the exact location via Global Positioning System (GPS) coordinates) associated with specific messages/complaints. The identified noise complaints were organized in a digital database and analyzed according to three criteria: first, the main types of the noise source (music, human factors, transport infrastructures); second, exterior or interior noise source and finally, date and time of the report, with the location of the Twitter user. This study supports the idea that lexicon-based analyses of large social media datasets may prove to be a useful adjunct or as a complement to existing noise pollution identification and surveillance strategies.

Keywords: Twitter; noise; annoyance; geolocation; noise classification (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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