Related Experiment Video
Updated: Oct 17, 2025

Microfluidic Devices for Characterizing Pore-scale Event Processes in Porous Media for Oil Recovery Applications
Published on: January 16, 2018
Forecasting the U.S. oil markets based on social media information during the COVID-19 pandemic
Binrong Wu1, Lin Wang1, Sirui Wang1
1School of Management, Huazhong University of Science and Technology, Wuhan, 430074, China.
Abstract:
Accurate oil market forecasting plays an important role in the theory and application of oil supply chain management for profit maximization and risk minimization. However, the coronavirus disease 2019 (COVID-19) has compelled governments worldwide to impose restrictions, consequently forcing the closure of most social and economic activities. The latter leads to the volatility of the oil markets and poses a huge challenge to oil market forecasting. Fortunately, the social media information can finely reflect oil market factors and exogenous factors, such as conflicts and political instability. Accordingly, this study collected vast online oil news and used convolutional neural network to extract relevant information automatically. Oil markets are divided into four categories: oil price, oil production, oil consumption, and oil inventory. A total of 16,794; 9,139; 8,314; and 8,548 news headlines were collected in four respective cases. Experimental results indicate that social media information contributes to the forecasting of oil price, oil production and oil consumption. The mean absolute percentage errors are respectively 0.0717, 0.0144 and 0.0168 for the oil price, production, and consumption prediction during the COVID-19 pandemic. Marketers must consider the impact of social media information on the oil or similar markets, especially during the COVID-19 outbreak.
Related Concept Videos
Steps in Outbreak Investigation
Residual Plots
When the residual values are plotted against the variable x, it is called a residual...
Pareto Chart
The Pareto chart is named after the Italian economist Vilfredo Pareto, who described the Pareto...
Predicting Reaction Outcomes
Prediction Intervals
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.

