Effective communication lies at the heart of social harmony and individual well-being. However, key areas of our society face profound challenges in how we talk about things, or to each other. In this talk, I will show how these challenges manifest: from the manner in which TV reporters discuss current events to online health discussions in banned Reddit communities, and interactions between law enforcement and communities of color during routine car stops. My research applies theories from linguistics and psychology to analyze patterns in such dialogue using large language models (LLMs), statistics, and experimental design. In this presentation, I will introduce three research studies that highlight how specific patterns in our language choices are predictive of real-world outcomes. First, I will illustrate how partisan divides in the language of America’s two major broadcasting news stations over the past decade directly correlate with semantic polarity trends on Twitter, empirically linking for the first time how online discussions are influenced by televised media. Second, I will show how “gists” or causal statements in social media discussions about pandemic health practices unveil underlying beliefs and attitudes, which in turn, can forecast broader health trends across the U.S. Finally, by examining the linguistic interactions captured from thousands of footages from police body-worn cameras, I demonstrate how the first 45 words spoken by a police officer during a car stop with a Black driver can be quite telling about how the stop will conclude. Persistent challenges in dialogue marked by tensions and biases can have wide-ranging implications for both individuals and society. These studies call for a broader awareness on the influence of our language choices across institutional, media, and online contexts.