Real-world unexpected outcomes predict city-level mood states and risk-taking behavior

A Ross Otto and Johannes C Eichstaedt

PLOS ONE, 2018, vol. 13, issue 11, 1-18

Abstract: Fluctuations in mood states are driven by unpredictable outcomes in daily life but also appear to drive consequential behaviors such as risk-taking. However, our understanding of the relationships between unexpected outcomes, mood, and risk-taking behavior has relied primarily upon constrained and artificial laboratory settings. Here we examine, using naturalistic datasets, how real-world unexpected outcomes predict mood state changes observable at the level of a city, in turn predicting changes in gambling behavior. By analyzing day-to-day mood language extracted from 5.2 million location-specific and public Twitter posts or ‘tweets’, we examine how real-world ‘prediction errors’—local outcomes that deviate positively from expectations—predict day-to-day mood states observable at the level of a city. These mood states in turn predicted increased per-person lottery gambling rates, revealing how interplay between prediction errors, moods, and risky decision-making unfolds in the real world. Our results underscore how social media and naturalistic datasets can uniquely allow us to understand consequential psychological phenomena.

Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0206923

DOI: 10.1371/journal.pone.0206923

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