MINING SOCIAL MEDIA TO ASSESS PUBLIC PERCEPTION OF WATER QUALITY
Abstract
Social media provides potentially new sources of information for the detection and
management of undesirable water quality events such as harmful algae blooms (HABs)
in surface waters. Current methods for identifying HAB include field sampling and
laboratory tests which are time-intensive and can cause a delay in the issuance of
warning advisories, resulting in public health consequences. The potential strengths
of social media as water quality indicators are that social media data can be collected
in real-time using Application Programming Interfaces (APIs) and is less expensive
compared to traditional water quality sampling methods. But the challenge lies in
understanding what water quality parameters the public perceives and responds to.
To address this challenge, we explored tweets (2016 – 2020) expressing negative sentiment
related to the water quality of Utah lake which is well-known for its algae blooms.
We used sentiment analysis, natural language processing, spatial interpolation, and
count regression modeling to evaluate temporal correlations of social media posts
obtained using Twitter API and water quality data collected by the Utah Department
of Environmental Quality. We found that the negative tweet counts were significantly
and positively associated with many of the perceivable water quality parameters studied
such as turbidity, chlorophyll-a, phytoplankton cell count, phytoplankton biovolume,
cyanobacteria cell count, and cyanobacteria biovolume. Surface samples for algae concentration
and population were also significantly related to the negative tweet counts while
the composite samples were not significant, thereby supporting the idea that the public
perceives and responds to the toxic water quality near the water surface. Our work
serves as a preliminary study that highlights the potential of using social media
for identifying water quality events in lakes. To achieve the ultimate goal of developing
a real-time public warning system, further studies should be conducted to develop
metrics that can translate social media sentiment and activity to a quantitative measurement
of water quality health.
Type
Master's projectPermalink
https://hdl.handle.net/10161/22666Citation
Do, Ha; Mishra, Prashank; & Yang, Longyi (2021). MINING SOCIAL MEDIA TO ASSESS PUBLIC PERCEPTION OF WATER QUALITY. Master's project, Duke University. Retrieved from https://hdl.handle.net/10161/22666.Collections
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