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.

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Video Annotation is a crucial process in computer science and social science alike. Many video annotation tools (VATs) offer a wide range of features for making annotation possible. We conducted an extensive survey of over 59 VATs and interviewed interdisciplinary researchers to evaluate the usability of VATs. Our findings suggest that most current VATs have overwhelming user interfaces, poor interaction techniques, and difficult-to-understand features. These often lead to longer annotation time, label inconsistencies, and user fatigue. We introduce FEVA, a video annotation tool with streamlined interaction techniques and a dynamic interface that makes labeling tasks easy and fast. FEVA focuses on speed, accuracy, and simplicity to make annotation quick, consistent, and straightforward. For example, annotators can control the speed and direction of the video and mark the onset and the offset of a label in real time with single key presses. In our user study, FEVA users, on average, require 36% less interaction than the most popular annotation tools (Advene, ANVIL, ELAN, VIA, and VIAN). The participants (N=32) rated FEVA as more intuitive and required less mental demand. The code and demo are available at http://www.snehesh.com/feva. Figure 1: "Speed Label" enables you to create annotations (red rectangle) in real-time by marking start (st p ) and end time (et p ) with a single key press respectively. FEVA automatically adjusts these times of the event annotation based on your reaction-time (∆r) in order to give you the most precise intended times (st i and et i ).