This study aims to develop computational techniques to analyze and identify points of tensions in interviews with survivors of the 1994 Rwandan genocide. Oral history interviews are a dialogical source composed of questions and answers, producing a conversational narrative. Yet survivor testimony is often approached as though the questions did not exist. This article examines a digital tool that helps us visualize and better understand the underlying interview dynamic that is the heart of oral history and qualitative research more generally. Our tension detection tool identifies those moments in the interview when the interviewer and interviewee are trying to pull the conversation in different directions. This is part of the natural give-and-take of the interview. Hedging, deflection, hesitation, and boosting are all critical components of this interviewer-interviewee tension. By making the interview dynamic central to our analysis, we aim to better understand how the interview dynamic shapes what is being said and what is left unsaid. In this study, we address key components of interview tension and propose a natural language processing model that can efficiently incorporate these components in text-based oral history interviews to identify tension points. With experiments on an annotated transcript, we verify the efficacy of our model. This model provides a framework that can be utilized in future research on the dialogic of the interview.
|Original language||English (US)|
|Journal||Digital Studies/ Le Champ Numerique|
|State||Published - 2022|
ASJC Scopus subject areas
- Arts and Humanities(all)
- Social Sciences(all)
- Computer Science Applications