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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Andrew Peplow & Justin Thomas & Aamna AlShehhi, 2021. "Noise Annoyance in the UAE: A Twitter Case Study via a Data-Mining Approach," IJERPH, MDPI, vol. 18(4), pages 1-9, February.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:4:p:2198-:d:504477
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    References listed on IDEAS

    as
    1. Freeman, Chris & Louca, Francisco, 2002. "As Time Goes By: From the Industrial Revolutions to the Information Revolution," OUP Catalogue, Oxford University Press, number 9780199251056.
    2. Jeremy Ginsberg & Matthew H. Mohebbi & Rajan S. Patel & Lynnette Brammer & Mark S. Smolinski & Larry Brilliant, 2009. "Detecting influenza epidemics using search engine query data," Nature, Nature, vol. 457(7232), pages 1012-1014, February.
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    Cited by:

    1. Luca Fredianelli & Peter Lercher & Gaetano Licitra, 2022. "New Indicators for the Assessment and Prevention of Noise Nuisance," IJERPH, MDPI, vol. 19(19), pages 1-5, October.
    2. Martin Zajac & Jiří Horák & Joaquín Osorio-Arjona & Pavel Kukuliač & James Haworth, 2022. "Public Transport Tweets in London, Madrid and Prague in the COVID-19 Period—Temporal and Spatial Differences in Activity Topics," Sustainability, MDPI, vol. 14(24), pages 1-25, December.
    3. Liza Lee & Ying-Sing Liu, 2021. "Training Effects and Intelligent Evaluated Pattern of the Holistic Music Educational Approach for Children with Developmental Delay," IJERPH, MDPI, vol. 18(19), pages 1-12, September.
    4. Enara Zarrabeitia-Bilbao & Rosa-María Rio-Belver & Izaskun Alvarez-Meaza & Itziar Martínez de Alegría-Mancisidor, 2022. "World Environment Day: Understanding Environmental Programs Impact on Society Using Twitter Data Mining," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 164(1), pages 263-284, November.

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