# Using four different online media sources to forecast the crude oil price

arXiv: https://arxiv.org/abs/2105.09154 · HTML: https://mlsanity.com/paper/2105.09154/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 87 references that are in the map.
- First posted on arXiv 19 May 2021.

## Builds on (most cited first, 4 of 4)

- [Using social network analysis to prevent money laundering](https://mlsanity.com/paper/2105.05793/) (2021)
- [Understanding News Geography and Major Determinants of Global News Coverage of Disasters](https://mlsanity.com/paper/1410.3710/) (2014)
- [Two Tales of the World: Comparison of Widely Used World News Datasets GDELT and EventRegistry](https://mlsanity.com/paper/1603.01979/) (2016)
- [Cultural Anthropology through the Lens of Wikipedia: Historical Leader Networks, Gender Bias, and News-based Sentiment](https://mlsanity.com/paper/1508.00055/) (2015)

## Built on by (most cited first, 5 of 5)

- [Measuring the impact of spammers on e-mail and Twitter networks](https://mlsanity.com/paper/2105.10256/) (2021)
- [Forecasting Crude Oil Price Using Event Extraction](https://mlsanity.com/paper/2111.09111/) (2021)
- [Assessing perceived organizational leadership styles through twitter text mining](https://mlsanity.com/paper/2105.11276/) (2021)
- [Look inside. Predicting stock prices by analysing an enterprise intranet social network and using word co-occurrence networks](https://mlsanity.com/paper/2105.11780/) (2021)
- [Hybrid Deep Learning Modeling Approach to Predict Natural Gas Consumption of Home Subscribers on Limited Data](https://mlsanity.com/paper/2510.02115/) (2025)

## Abstract (arXiv)

This study looks for signals of economic awareness on online social media and tests their significance in economic predictions. The study analyses, over a period of two years, the relationship between the West Texas Intermediate daily crude oil price and multiple predictors extracted from Twitter, Google Trends, Wikipedia, and the Global Data on Events, Language, and Tone database (GDELT). Semantic analysis is applied to study the sentiment, emotionality and complexity of the language used. Autoregressive Integrated Moving Average with Explanatory Variable (ARIMAX) models are used to make predictions and to confirm the value of the study variables. Results show that the combined analysis of the four media platforms carries valuable information in making financial forecasting. Twitter language complexity, GDELT number of articles and Wikipedia page reads have the highest predictive power. This study also allows a comparison of the different fore-sighting abilities of each platform, in terms of how many days ahead a platform can predict a price movement before it happens. In comparison with previous work, more media sources and more dimensions of the interaction and of the language used are combined in a joint analysis.