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This paper presents our contributions to the MediaEval 2021 task namely"WaterMM: Water Quality in Social Multimedia". The task aims at analyzingsocial media posts relevant to water quality with particular focus on theaspects like watercolor, smell, taste, and related illnesses. To this aim, amultimodal dataset containing both textual and visual information along withmeta-data is provided. Considering the quality and quantity of availablecontent, we mainly focus on textual information by employing three differentmodels individually and jointly in a late-fusion manner. These models include(i) Bidirectional Encoder Representations from Transformers (BERT), (ii)Robustly Optimized BERT Pre-training Approach (XLM-RoBERTa), and a (iii) customLong short-term memory (LSTM) model obtaining an overall F1-score of 0.794,0.717, 0.663 on the official test set, respectively. In the fusion scheme, allthe models are treated equally and no significant improvement is observed inthe performance over the best performing individual model.

NLP Techniques for Water Quality Analysis in Social Media Content