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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Javier Jiménez-Cabas & Lizeth Torres & Jorge de J. Lozoya-Santos, 2023. "Twitter Data Mining for the Diagnosis of Leaks in Drinking Water Distribution Networks," Sustainability, MDPI, vol. 15(6), pages 1-16, March.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:6:p:5113-:d:1096618
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    References listed on IDEAS

    as
    1. Li, Xin & Wen, Yang & Jiang, Jiaojiao & Daim, Tugrul & Huang, Lucheng, 2022. "Identifying potential breakthrough research: A machine learning method using scientific papers and Twitter data," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    2. Rashid Behzadidoost & Mahdieh Hasheminezhad & Mohammad Farshi & Vali Derhami & Farinaz Alamiyan-Harandi, 2022. "A framework for text mining on Twitter: a case study on joint comprehensive plan of action (JCPOA)- between 2015 and 2019," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(5), pages 3053-3084, October.
    3. Abeed Sarker & Karen O’Connor & Rachel Ginn & Matthew Scotch & Karen Smith & Dan Malone & Graciela Gonzalez, 2016. "Social Media Mining for Toxicovigilance: Automatic Monitoring of Prescription Medication Abuse from Twitter," Drug Safety, Springer, vol. 39(3), pages 231-240, March.
    4. Sophie E. Jordan & Sierra E. Hovet & Isaac Chun-Hai Fung & Hai Liang & King-Wa Fu & Zion Tsz Ho Tse, 2018. "Using Twitter for Public Health Surveillance from Monitoring and Prediction to Public Response," Data, MDPI, vol. 4(1), pages 1-20, December.
    5. Enara Zarrabeitia-Bilbao & Rosa-María Rio-Belver & Izaskun Alvarez-Meaza & Itziar Martínez de Alegría-Mancisidor, 2022. "World Environment Day: Understanding Environmental Programs Impact on Society Using Twitter Data Mining," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 164(1), pages 263-284, November.
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