  This paper focuses on a very important societal challenge of water quality
analysis. Being one of the key factors in the economic and social development
of society, the provision of water and ensuring its quality has always remained
one of the top priorities of public authorities. To ensure the quality of
water, different methods for monitoring and assessing the water networks, such
as offline and online surveys, are used. However, these surveys have several
limitations, such as the limited number of participants and low frequency due
to the labor involved in conducting such surveys. In this paper, we propose a
Natural Language Processing (NLP) framework to automatically collect and
analyze water-related posts from social media for data-driven decisions. The
proposed framework is composed of two components, namely (i) text
classification, and (ii) topic modeling. For text classification, we propose a
merit-fusion-based framework incorporating several Large Language Models (LLMs)
where different weight selection and optimization methods are employed to
assign weights to the LLMs. In topic modeling, we employed the BERTopic library
to discover the hidden topic patterns in the water-related tweets. We also
analyzed relevant tweets originating from different regions and countries to
explore global, regional, and country-specific issues and water-related
concerns. We also collected and manually annotated a large-scale dataset, which
is expected to facilitate future research on the topic.
