AUTHOR=Giorgi Salvatore , Jones Jason Jeffrey , Buffone Anneke , Eichstaedt Johannes C. , Crutchley Patrick , Yaden David B. , Elstein Jeanette , Zamani Mohammadzaman , Kregor Jennifer , Smith Laura , Seligman Martin E. P. , Kern Margaret L. , Ungar Lyle H. , Schwartz H. Andrew 

TITLE=Quantifying generalized trust in individuals and counties using language

JOURNAL=Frontiers in Social Psychology

VOLUME=Volume 2 - 2024

YEAR=2024

URL=https://www.frontiersin.org/journals/social-psychology/articles/10.3389/frsps.2024.1384262

DOI=10.3389/frsps.2024.1384262

ISSN=2813-7876

ABSTRACT=Trust is predictive of civic cooperation and economic growth. Recently, the US public has demonstrated increased partisan division and a surveyed decline in trust of institutions. There is a need to quantify individual and community levels of trust unobtrusively and at scale. Using observations of language across 16,000 Facebook users along with their self-reported generalized trust score, we develop and evaluate a language-based assessment of generalized trust. We then applied the assessment over 1.6 billion geotagged tweets collected between 2009 and 2015 and derived estimates of trust across 2,041 US counties. We find generalized trust was associated with more affiliative words ("love", "we", "friends") and less angry words ("hate", "stupid"), but only had a weak association with social words primarily driven by strong negative associations with general othering terms ("they", "people"). At the county level, associations with CDC and Gallup surveys suggest that people in high-trust counties were physically healthier and more satisfied with their community and their lives. Our study demonstrates that generalized trust levels can be estimated from language as a low-cost, unobtrusive method to monitor variations in trust in large populations.