With increased availability of information in modern societies, individuals are often faced with complex decisions regarding how to integrate and judge the veracity of available information. Generally, these issues have been approached using computational techniques to "detect" and "reduce" the spread of information across social media. However, few researchers have examined theoretically-grounded characteristics of misinformation. The purpose of this chapter is to examine this phenomenon through the lens of discourse processing theories, which emphasize interactions among features of the discourse, the reader, and the context. We describe a proof of concept on how dynamical systems modeling combined with computational linguistics has strong potential to reveal underlying characteristics of the spread of misinformation. Additionally, we discuss potential directions for future research, as well as implications for interventions to help students accurately process information in the modern digital age. We call for research using a combination of computational linguistics, telemetry, and dynamical systems analytics in order to better understand the temporal organization of text and the spread of misinformation. [This paper was a chapter in "Misinformation and Fake News in Education" (p159-185). 2019.]