@article{ref1,
title="Real-world unexpected outcomes predict city-level mood states and risk-taking behavior",
journal="PLoS one",
year="2018",
author="Otto, A. Ross and Eichstaedt, Johannes C.",
volume="13",
number="11",
pages="e0206923-e0206923",
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.<p /> <p>Language: en</p>",
language="en",
issn="1932-6203",
doi="10.1371/journal.pone.0206923",
url="http://dx.doi.org/10.1371/journal.pone.0206923"
}