Evidence of Things Not Seen: A Semi-Automated Descriptive Phrase and Frame Analysis of Texts about the Herbicide Agent Orange

From 1961 to 1971 the United States and the Republic of South Vietnam used chemicals to defoliate the coastal and upload forest areas of Viet Nam. The most notorious of these chemicals was named Agent Orange, a weaponized herbicide made up of two chemicals that, when combined, produced a toxic byproduct called TCDD-dioxin. Studied suggest that TCDD-dioxin causes significant human health problems in exposed American and Vietnamese veterans, and possibly their children (Agency, U.S. Environmental Protection, 2011). In the years since the end of the Vietnam War, volumes of discourse about Agent Orange has been generated, much of which is now digitally archived and machine-readable, providing rich sites of study ideal for “big data” text mining, extraction and computation. This study uses a combination of tools and text mining scripts developed in Python to study the descriptive phrases four discourse communities used across 45 years of discourse to talk about key issues in the debates over Agent Orange. Findings suggests these stakeholders describe and frame in significantly different ways, with Congress focused on taking action, the New York Times article and editorial corpus focused on controversy, and the Vietnamese News Agency focused on victimization. Findings also suggest that while new tools and methods make lighter work of mining large sets of corpora, a mixed-methods approach yields the most reliable insights. Though fully automated text analysis is still a distant reality, this method was designed to study potential effects of rhetoric on public policy and advocacy initiatives across large corpora of texts and spans of time