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Oil Market Shocks and Public Mood: Evidence from Daily Geotagged Twitter Sentiment

Author

Listed:
  • Onur Polat

    (Institute of Informatics, Hacettepe University, Beytepe Campus, 06800 Cankaya, Ankara, Turkiye)

  • Rangan Gupta

    (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa)

  • Christian Pierdzioch

    (Department of Economics, Helmut Schmidt University, Holstenhofweg 85, P.O.B. 700822, 22008 Hamburg, Germany)

Abstract

We examine whether and how structural oil market shocks affect expressed public mood. To this end, we combine the daily forward-looking demand, current demand, and supply shocks of Gazzani, Venditti, and Veronese (2024) with the Twitter Sentiment Geographical Index, a daily measure of expressed sentiment built from geotagged tweets, from January 2012 to June 2023. We estimate lag- augmented local projections for a world index, a panel of 126 countries, and, to assess the role of disaggregation, the United States at the country, state, and county levels. Sentiment does not react to any of the shocks on impact. The cumulative responses, however, reveal a gradual adjustment. Forward-looking demand shocks, which capture news about global growth prospects and risk appetite, raise sentiment persistently, with the response building up over about six months, and persisting for up to a year; the sign of this response, in turn, is stable across aggregation levels, samples, and inference methods. Oil supply shocks raise sentiment briefly in the world and U.S. series, after which their point estimates turn negative at horizons of seven to twelve months.

Suggested Citation

  • Onur Polat & Rangan Gupta & Christian Pierdzioch, 2026. "Oil Market Shocks and Public Mood: Evidence from Daily Geotagged Twitter Sentiment," Working Papers 202631, University of Pretoria, Department of Economics.
  • Handle: RePEc:pre:wpaper:202631
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    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • D91 - Microeconomics - - Micro-Based Behavioral Economics - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making
    • E71 - Macroeconomics and Monetary Economics - - Macro-Based Behavioral Economics - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on the Macro Economy
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy

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