Version 1
: Received: 20 October 2023 / Approved: 23 October 2023 / Online: 23 October 2023 (16:15:23 CEST)
How to cite:
Zagal-Flores, R.; Mata-Rivera, F.; Torres-Ruiz, M.; Benitez-Valerio, V.S.; Quintero, R.; Guzmán, G.; Juárez-Gambino, J.O. Analysis of Open-Social Data Behavior Concerning Gasoline Stealing: A Case Study of the Mexican Petroleum Crisis. Preprints2023, 2023101450. https://doi.org/10.20944/preprints202310.1450.v1
Zagal-Flores, R.; Mata-Rivera, F.; Torres-Ruiz, M.; Benitez-Valerio, V.S.; Quintero, R.; Guzmán, G.; Juárez-Gambino, J.O. Analysis of Open-Social Data Behavior Concerning Gasoline Stealing: A Case Study of the Mexican Petroleum Crisis. Preprints 2023, 2023101450. https://doi.org/10.20944/preprints202310.1450.v1
Zagal-Flores, R.; Mata-Rivera, F.; Torres-Ruiz, M.; Benitez-Valerio, V.S.; Quintero, R.; Guzmán, G.; Juárez-Gambino, J.O. Analysis of Open-Social Data Behavior Concerning Gasoline Stealing: A Case Study of the Mexican Petroleum Crisis. Preprints2023, 2023101450. https://doi.org/10.20944/preprints202310.1450.v1
APA Style
Zagal-Flores, R., Mata-Rivera, F., Torres-Ruiz, M., Benitez-Valerio, V.S., Quintero, R., Guzmán, G., & Juárez-Gambino, J.O. (2023). Analysis of Open-Social Data Behavior Concerning Gasoline Stealing: A Case Study of the Mexican Petroleum Crisis. Preprints. https://doi.org/10.20944/preprints202310.1450.v1
Chicago/Turabian Style
Zagal-Flores, R., Giovanni Guzmán and Joel Omar Juárez-Gambino. 2023 "Analysis of Open-Social Data Behavior Concerning Gasoline Stealing: A Case Study of the Mexican Petroleum Crisis" Preprints. https://doi.org/10.20944/preprints202310.1450.v1
Abstract
At the beginning of 2019, the petroleum crisis impacted many economies dependent on this industry. The Mexican government started programs to identify points and government officials involved in the gasoline stealing from PEMEX (Petróleos Mexicanos), the country’s leading government petroleum company. The programs consisted of supervising and monitoring the Mexican country network of gasoline ducts to detect points where gasoline was being stolen. Consequently, large urban regions faced a lack and shortage of gasoline. This situation generated several reactions in social media and many open data in news media. Although the government provided open data about stealing gasoline locations related to crimes, it did not analyze the collected data to identify patterns, insights, and the spatio-temporal characterization of this phenomenon. This paper presents a study to deal with the regional semantics described in the social media locations of gasoline stealing. Thus, a framework to discover the trends that emerge from social media and how it is correlated with the government’s open data is also presented—the proposed methodology used machine learning techniques based on linguistic and semantic technologies. The analysis was applied to a dataset of 24,317 geo-referenced tweets. The obtained results reflected the Mexican thinking opinion regarding discovered social topics, polarization maps, and regional insights. According to discovered trends, there were long fuel lines between 1.5 and 5 kilometers (on average) at fuel stations in different Mexican states.
Keywords
semantic and linguistic technologies; spatial data mining; spatial data analytics; spatio-temporal characterization; social media
Subject
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
,
,
