When danger strikes: A linguistic tool for tracking America’s collective response to threats

Virginia Choi, Snehesh Shrestha, Xinyue Pan, Michele J Gelfand

Abstract

In today’s vast digital landscape, people are constantly exposed to threatening language, which attracts attention and activates the human brain’s fear circuitry. However, to date, we have lacked the tools needed to identify threatening language and track its impact on human groups. To fill this gap, we developed a threat dictionary, a computationally derived linguistic tool that indexes threat levels from mass communication channels. We demonstrate this measure’s convergent validity with objective threats in American history, including violent conflicts, natural disasters, and pathogen outbreaks such as the COVID-19 pandemic. Moreover, the dictionary offers predictive insights on US society’s shifting cultural norms, political attitudes, and macroeconomic activities. Using data from newspapers that span over 100 years, we found change in threats to be associated with tighter social norms and collectivistic values, stronger approval of sitting US presidents, greater ethnocentrism and conservatism, lower stock prices, and less innovation. The data also showed that threatening language is contagious. In all, the language of threats is a powerful tool that can inform researchers and policy makers on the public’s daily exposure to threatening language and make visible interesting societal patterns across American history.

Acknowledgments

We thank both Dylan Pieper and Ioanna Galani for their research assistance. The present research was funded in part by Office of Naval Research grant N000141912407 (M.J.G.). The information in this article does not imply or constitute an endorsement of the views therein by the Office of Naval Research, US Navy, or Department of Defense.

Bibtex

If you use this code in your research or benefited from this work, please cite our work:

@article{choi2022danger,
        title={When danger strikes: A linguistic tool for tracking America's collective response to threats},
        author={Choi, Virginia K and Shrestha, Snehesh and Pan, Xinyue and Gelfand, Michele J},
        journal={Proceedings of the National Academy of Sciences},
        volume={119},
        number={4},
        pages={e2113891119},
        year={2022},
        publisher={National Acad Sciences}
    }

License

Threat Dictionary generation code is freely available for non-commercial and research use and may be redistributed under the conditions detailed on the license page. If you have any questions, please get in touch with me at snehesh@umd.edu.