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Impact of temperature on expressed sentiments in social media: evidence from a Latin American country

Published online by Cambridge University Press:  07 April 2025

José Daniel Aromí ,
Mariana Conte Grand ,
Mariano Rabassa Open the ORCID record for Mariano Rabassa [Opens in a new window]  and
Julie Rozenberg
José Daniel Aromí
Affiliation:
Facultad de Ciencias Económicas, Pontificia Universidad Católica Argentina, Buenos Aires, Argentina Instituto Interdisciplinario de Economía Política (IIEP), Facultad de Ciencias Económicas, Universidad de Buenos Aires, Buenos Aires, Argentina
Mariana Conte Grand
Affiliation:
Environment Department, Latin America and the Caribbean region, The World Bank, Washington, DC, USA Departamento de Economía, Universidad del CEMA, Buenos Aires, Argentina
Mariano Rabassa*
Affiliation:
Facultad de Ciencias Económicas, Pontificia Universidad Católica Argentina, Buenos Aires, Argentina
Julie Rozenberg
Affiliation:
Environment Department, Europe and Central Asia region, The World Bank, Washington, DC, USA
*
*Corresponding author: Mariano Rabassa; Email: [email protected]
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Abstract

This study examines the impact of temperature on human well-being using approximately 80 million geo-tagged tweets from Argentina spanning 2017–2022. Employing text mining techniques, we derive two quantitative estimators: sentiments and a social media aggression index. The Hedonometer Index measures overall sentiment, distinguishing positive and negative ones, while social media aggressive behavior is assessed through profanity frequency. Non-linear fixed effects panel regressions reveal a notable negative causal association between extreme heat and the overall sentiment index, with a weaker relationship found for extreme cold. Our results highlight that, while heat strongly influences negative sentiments, it has no significant effect on positive ones. Consequently, the overall impact of extremely high temperatures on sentiment is predominantly driven by heightened negative feelings in hot conditions. Moreover, our profanity index exhibits a similar pattern to that observed for negative sentiments.

Keywords

natural language processingsocial mediasentiment analysistemperatureX-Twitter

JEL classification

Q54: Climate • Natural Disasters and Their Management • Global Warming C13: Estimation: General I31: General Welfare, Well-Being
Type
Research Article
Information
Environment and Development Economics , First View , pp. 1 - 25
Copyright
© The World Bank Group, 2025. Published by Cambridge University Press

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