Twitter Data Mining for the Diagnosis of Leaks in Drinking Water Distribution Networks

Javier Jiménez-Cabas, Lizeth Torres () and Jorge de J. Lozoya-Santos
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Javier Jiménez-Cabas: Departamento de Ciencias de la Computación y Electrónica, Universidad de la Costa, Barranquilla 080002, Colombia
Lizeth Torres: Instituto de Ingeniería, Universidad Nacional Autónoma de México, Ciudad de México 04510, Mexico
Jorge de J. Lozoya-Santos: Departamento de Mecatrónica, Escuela de Ingeniería y Ciencias, Tecnológico de Monterrey, Monterrey 64849, Mexico

Sustainability, 2023, vol. 15, issue 6, 1-16

Abstract: This article presents a methodology for using data from social networks, specifically from Twitter , to diagnose leaks in drinking water distribution networks. The methodology involves the collection of tweets from citizens reporting leaks, the extraction of information from the tweets , and the processing of such information to run the diagnosis. To demonstrate the viability of this methodology, 358 Twitter leak reports were collected and analyzed in Mexico City from 1 May to 31 December 2022. From these reports, leak density and probability were calculated, which are metrics that can be used to develop forecasting algorithms, identify root causes, and program repairs. The calculated metrics were compared with those calculated through telephone reports provided by SACMEX, the entity that manages water in Mexico City. Results show that metrics obtained from Twitter and phone reports were highly comparable, indicating the usefulness and reliability of social media data for diagnosing leaks.

Keywords: leak diagnosis; social sensors; social network data; Twitter; text mining (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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