Social Media as an Instant Source of Feedback on Water Quality

This article focuses on an important environmental challenge: the measurement of water quality, by analyzing the potential of social media to be harnessed as an immediate source of feedback. The goal of the work is to automatically analyze and retrieve social media posts relevant to water quality, with particular attention to posts describing different aspects of water quality, such as color, smell, taste, and water-related illnesses. To this aim, we propose a novel framework incorporating different preprocessing, data augmentation, and classification techniques. We use three neural network (NN) architectures for our framework, namely: 1) bidirectional encoder representations from transformers (BERTs); 2) robustly optimized BERT pretraining approach (XLM-RoBERTa); and 3) a custom long short-term memory (LSTM) model. These are employed in a merit-based fusion scheme. For merit-based weight assignment to the models, several optimization and search techniques are compared, including a particle swarm optimization (PSO), genetic algorithm (GA), brute force (BF), Nelder–Mead, and Powell’s optimization methods. We also provide an evaluation of the individual models where the highest
$F1$
-score of 0.81 is obtained with the BERT model. Overall, in merit-based fusion, better results are obtained with BF achieving an
$F1$
-score score of 0.852. We also provide a comparison against existing methods, where a significant improvement for our proposed solutions is obtained. We believe such a rigorous analysis of this relatively new topic will provide a baseline for future research.