Noise Annoyance in the UAE : A Twitter Case Study via a Data-Mining Approach
Peplow, Andrew LU
; Thomas, Justin
and AlShehhi, Aamna
(2021)
In International Journal of Environmental Research and Public Health
18(4).
- 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... (More)
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.
(Less)
- author
- Peplow, Andrew
LU
; Thomas, Justin
and AlShehhi, Aamna
- organization
- publishing date
- 2021-02-23
- type
- Contribution to journal
- publication status
- published
- subject
- in
- International Journal of Environmental Research and Public Health
- volume
- 18
- issue
- 4
- article number
- 2198
- publisher
- MDPI AG
- external identifiers
-
- scopus:85101245727
- pmid:33672320
- ISSN
- 1660-4601
- DOI
- 10.3390/ijerph18042198
- language
- English
- LU publication?
- yes
- id
- a872f231-3191-4375-96d0-c0fc3c1f4333
- date added to LUP
- 2021-03-08 09:46:52
- date last changed
- 2024-04-18 02:58:16
@article{a872f231-3191-4375-96d0-c0fc3c1f4333,
abstract = {{<p>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.</p>}},
author = {{Peplow, Andrew and Thomas, Justin and AlShehhi, Aamna}},
issn = {{1660-4601}},
language = {{eng}},
month = {{02}},
number = {{4}},
publisher = {{MDPI AG}},
series = {{International Journal of Environmental Research and Public Health}},
title = {{Noise Annoyance in the UAE : A Twitter Case Study via a Data-Mining Approach}},
url = {{http://dx.doi.org/10.3390/ijerph18042198}},
doi = {{10.3390/ijerph18042198}},
volume = {{18}},
year = {{2021}},
}