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

John Burns*, Tom Kelsey, Carl Donovan

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

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.
Original languageEnglish
Pages (from-to)122–138
Number of pages17
JournalJournal of Social Media Research
Volume2
Issue number2
DOIs
Publication statusPublished - 2 Jun 2025

Keywords

  • X/Twitter
  • Ukraine War
  • Sentiment analysis
  • Financial market
  • Analysis
  • Multilingual analysis

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