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Open AccessCommunication

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

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Division of Engineering Acoustics, Department of Construction Sciences, Lund University, 221 00 Lund, Sweden
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College of Natural and Health Sciences, Zayed University, Abu Dhabi 144534, United Arab Emirates
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Electrical and Computer Engineering, Khalifa University, Abu Dhabi 127788, United Arab Emirates
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Author to whom correspondence should be addressed.
Academic Editors: Gaetano Licitra, Luca Fredianelli and Peter Lercher
Int. J. Environ. Res. Public Health 2021, 18(4), 2198; https://doi.org/10.3390/ijerph18042198
Received: 30 December 2020 / Revised: 11 February 2021 / Accepted: 13 February 2021 / Published: 23 February 2021
(This article belongs to the Special Issue New Indicators for the Assessment and Prevention of Noise Nuisance)
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. View Full-Text
Keywords: Twitter; noise; annoyance; geolocation; noise classification Twitter; noise; annoyance; geolocation; noise classification
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MDPI and ACS Style

Peplow, A.; Thomas, J.; AlShehhi, A. Noise Annoyance in the UAE: A Twitter Case Study via a Data-Mining Approach. Int. J. Environ. Res. Public Health 2021, 18, 2198. https://doi.org/10.3390/ijerph18042198

AMA Style

Peplow A, Thomas J, AlShehhi A. Noise Annoyance in the UAE: A Twitter Case Study via a Data-Mining Approach. International Journal of Environmental Research and Public Health. 2021; 18(4):2198. https://doi.org/10.3390/ijerph18042198

Chicago/Turabian Style

Peplow, Andrew; Thomas, Justin; AlShehhi, Aamna. 2021. "Noise Annoyance in the UAE: A Twitter Case Study via a Data-Mining Approach" Int. J. Environ. Res. Public Health 18, no. 4: 2198. https://doi.org/10.3390/ijerph18042198

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