ORCID Identifiers
Document Type
Article
Source of Publication
International Journal of Environmental Research and Public Health
Publication Date
2-2-2021
Abstract
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. 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.
DOI Link
ISSN
Publisher
MDPI AG
Volume
18
Issue
4
Last Page
10
Disciplines
Computer Sciences
Keywords
Annoyance, Geolocation, Noise, Noise classification, Twitter
Scopus ID
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Peplow, Andrew; Thomas, Justin; and Alshehhi, Aamna, "Noise annoyance in the UAE: A twitter case study via a data-mining approach" (2021). All Works. 4055.
https://zuscholars.zu.ac.ae/works/4055
Indexed in Scopus
yes
Open Access
yes
Open Access Type
Gold: This publication is openly available in an open access journal/series