The talk illustrates a quantitative social science approach to texts developed by the author, Quantitative Narrative Analysis (QNA). QNA relies on computer-assisted story grammars to analyze narrative, where a story grammar is the simple Subject-Verb-Object (SVO) structure. In narrative, Subjects are typically social actors – individuals, groups, organizations – Verbs are actions, and Objects are both social actors and physical and abstract objects. To each of the three SVO components one can add several attributes to capture the complexity of stories (e.g., name of an individual, number of actors in a group, time and space of action). The talk will illustrate the power of the approach using data collected by the author from newspapers on the rise of Italian fascism (1919–1922) (300,000 SVOs) and Georgia lynchings (1875–1930) (7,000 SVOs). It will show how narrative data lend themselves to cutting-edge tools of data visualization and analysis as network graphs and maps in Google Earth and other GIS software. It will also show how QNA data provide the basis for fascinating digital humanities projects.