Water quality issues: Can we detect a creeping crisis with social media data?

Abstract

Social media data have been widely used in disaster management and particularly for the early detection of disaster emergencies. However, apart from sudden crises, there are also creeping crises, which are less evident but can be equally threatening to human lives, such as water pollution. The question raised is whether social media data can be used for discovering issues of water quality. In this work we attempt to answer this question by collecting posts from Twitter during the period of one year, which contain keywords about water quality, and applying three well-known techniques for event detection, i.e. Z-score, STA/LTA, and DBSCAN. A detailed presentation of the detected events, both relevant and not relevant, is given to provide more insight and proves that it is indeed feasible to identify water quality events with social media data. In addition, a quantitative evaluation of the three methods, in terms of precision, shows the superiority of Z-score for this particular topic.