Predicting gasoline shortage during disasters using social media

Abhinav Khare (), Qing He () and Rajan Batta ()
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Abhinav Khare: University at Buffalo
Qing He: University at Buffalo
Rajan Batta: University at Buffalo

OR Spectrum: Quantitative Approaches in Management, 2020, vol. 42, issue 3, No 5, 693-726

Abstract: Abstract Shortage of gasoline is a common phenomenon during onset of forecasted disasters like hurricanes. Prediction of future gasoline shortage can guide agencies in pushing supplies to the correct regions and mitigating the shortage. We demonstrate how to incorporate social media data into gasoline supply decision making. We develop a systematic approach to examine social media posts like tweets and sense future gasoline shortage. We build a four-stage shortage prediction methodology. In the first stage, we filter out tweets related to gasoline. In the second stage, we use an SVM-based tweet classifier to classify tweets about the gasoline shortage, using unigrams and topics identified using topic modeling techniques as our features. In the third stage, we predict the number of future tweets about gasoline shortage using a hybrid loss function, which is built to combine ARIMA and Poisson regression methods. In the fourth stage, we employ Poisson regression to predict shortage using the number of tweets predicted in the third stage. To validate the methodology, we develop a case study that predicts the shortage of gasoline, using tweets generated in Florida during the onset and post landfall of Hurricane Irma. We compare the predictions to the ground truth about gasoline shortage during Irma, and the results are very accurate based on commonly used error estimates.

Keywords: Social media analytics; Gasoline shortage prediction modeling; Disaster management; Hybrid loss function; Hurricane Irma (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s00291-019-00559-8

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