Dictionary Virginia Choi, Snehesh Shrestha, Xinyue Pan, & Michele Gelfand
Threat Meter Dylan Pieper
In today’s world, ominous warnings about imminent threats are prevalent in advertisements, political rhetoric, and newscasts. Especially within the vast digital landscape, we absorb a torrent of online content on platforms, apps, and news feeds designed to elicit our fear of potential threats. By raising the alarm on our impending doom, threat-related words and messages can instantly attract our attention—activating the fear circuitry in the human brain that collates and accentuates valuable survival information. The rise of social media, in particular, has fueled research on the negative effects of fear mongering and the mass circulation of misinformation. Whether the goal is to inform or exploit, claims about threats are pervasive in public discourse and there is an urgent need for both researchers and policymakers to understand its social consequences. However, to date, our ability to detect threatening language in texts is limited by the lack of accessible, validated linguistic dictionaries
To understand the implications of threats broadcasted through mass communication channels, such as the news or social media, we created one of the first computational linguistic tools that indexes threat levels from texts using natural language processing (NLP) tools. Thus, the following threat meter can be used to assess threat in any text by copying and pasting text into the boxes on the left. What's more, you can compare two different pieces of text side by side.
This dictionary was designed to diagnose threatening language in any text that interests you. Details regarding the development and validation of the dictionary can be found in our PNAS paper.
The dictionary is free to use and is currently available in English.
We ask that when discussing the dictionary in your own work that you cite our publication. Please don’t hesitate to contact us with any questions.