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

Area of Science:

• Environmental Health
• Public Health Surveillance
• Social Media Analytics

Background:

• Noise pollution is a significant global public health issue, linked to sleep disturbance, hearing loss, and cardiovascular problems.
• Social media platforms are increasingly used by individuals to voice concerns about noise pollution.

Purpose of the Study:

• To explore the utility of social media data for identifying and monitoring noise annoyance in the United Arab Emirates (UAE).
• To assess the potential of social media analysis as a complementary tool for existing noise pollution surveillance strategies.

Main Methods:

• Utilized a large dataset of over eight million Twitter messages from the UAE in 2015.
• Employed a search algorithm to identify noise-related complaints and extracted Global Positioning System (GPS) coordinates where available.
• Categorized identified noise complaints by source type (music, human factors, transport), location (exterior/interior), and temporal data.

Main Results:

• Successfully identified and geolocated numerous noise-related complaints from Twitter data.
• Analyzed the types and patterns of noise annoyance reported across the UAE.
• Demonstrated the feasibility of using social media data for noise pollution monitoring.

Conclusions:

• Lexicon-based analysis of social media data is a viable supplementary method for identifying and monitoring noise pollution.
• Social media can provide valuable insights into public perception and the spatial distribution of noise annoyance.
• This approach can enhance existing strategies for noise pollution management and public health interventions.