Multilingual X/Twitter sentiment analysis of geopolitical risk using granger causality focusing on the Ukraine war and financial markets

This paper investigates the changes in financial assets and markets from December 1st, 2021, to April 30th, 2022, during the start of the Ukraine War. These dates roughly correspond to the prelude to the War in December 2021 to a few weeks after Russian troops withdrew from the Kyiv area on April 7th, 2022. We used the Goldstein 1992 Results Table to create Positive and Negative Geopolitical Risk bigrams (Goldstein, 1992). With these bigrams, we collected over 3.6 million tweets during our research period in seven different languages (English, Spanish, French, Portuguese, Arabic, Japanese, and Korean) to capture worldwide reaction to the Ukraine War. Using various sentiment analysis methods, we constructed a time series of changes in the daily Geopolitical Risk sentiment. We explored its relationship to 39 financial assets and markets at various time lags. We found through Granger causality that the geopolitical risk time series contained predictive information on several assets and market changes.

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Publication Details

Journal
Journal of Social Media Research
Published
2025-06-02
DOI
https://doi.org/10.29329/jsomer.23
Primary Topic
Sentiment Analysis and Opinion Mining
Type
article
Field-Weighted Citation Impact
0.00

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article

Multilingual X/Twitter sentiment analysis of geopolitical risk using granger causality focusing on the Ukraine war and financial markets

Tom Kelsey, John Burns, Carl Donovan
Journal of Social Media Research
Sentiment Analysis and Opinion Mining
article

Multilingual X/Twitter sentiment analysis of geopolitical risk using granger causality focusing on the Ukraine war and financial markets

Tom Kelsey, John Burns, Carl Donovan
article en

Abstract

This paper investigates the changes in financial assets and markets from December 1st, 2021, to April 30th, 2022, during the start of the Ukraine War. These dates roughly correspond to the prelude to the War in December 2021 to a few weeks after Russian troops withdrew from the Kyiv area on April 7th, 2022. We used the Goldstein 1992 Results Table to create Positive and Negative Geopolitical Risk bigrams (Goldstein, 1992). With these bigrams, we collected over 3.6 million tweets during our research period in seven different languages (English, Spanish, French, Portuguese, Arabic, Japanese, and Korean) to capture worldwide reaction to the Ukraine War. Using various sentiment analysis methods, we constructed a time series of changes in the daily Geopolitical Risk sentiment. We explored its relationship to 39 financial assets and markets at various time lags. We found through Granger causality that the geopolitical risk time series contained predictive information on several assets and market changes.

Journal of Social Media ResearchVol. 2(2)
University of St Andrews (GB)
University of St Andrews
Openalex Percentile: Top 94%
Sentiment Analysis and Opinion Mining
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Multilingual X/Twitter sentiment analysis of geopolitical risk using granger causality focusing on the Ukraine war and financial markets — Tom Kelsey, John Burns, et al. · Journal of Social Media Research (2025) | TGRS Research Map | TGRS