TY - JOUR
T1 - Multilingual X/Twitter sentiment analysis of geopolitical risk using Granger causality focusing on the Ukraine war and financial markets
AU - Burns, John
AU - Kelsey, Tom
AU - Donovan, Carl
N1 - Funding: This work was supported by the School of Computer Science of the University of St Andrews.
PY - 2025/6/2
Y1 - 2025/6/2
N2 - 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.
AB - 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.
KW - X/Twitter
KW - Ukraine War
KW - Sentiment analysis
KW - Financial market
KW - Analysis
KW - Multilingual analysis
U2 - 10.29329/jsomer.23
DO - 10.29329/jsomer.23
M3 - Review article
SN - 3062-0945
VL - 2
SP - 122
EP - 138
JO - Journal of Social Media Research
JF - Journal of Social Media Research
IS - 2
ER -