Tension Analysis in Survivor Interviews: A Computational Approach
Bibliographic record
Abstract
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.Cette étude vise à développer des techniques computationnelles pour analyser et identifier des points de tensions dans des interviews avec des survivants du génocide rwandais en 1994. Les interviews d’histoire orale sont une source dialogique composée de questions et de réponses, ce qui produit une narration conversationnelle. Cependant, le témoignage de survivant est souvent traité comme si les questions n’existaient pas. Cet article examine un outil numérique qui nous aide à visualiser et à mieux comprendre la dynamique d’interview sous-jacente qui est au cœur de l’histoire orale et, plus généralement, au cœur de la recherche qualitative. Notre outil détecteur de tensions identifie ces moments dans l’interview lorsque l’intervieweur et l’interviewé sont en train d’essayer de guider la conversation dans des directions différentes. Cela fait partie de l’interaction bidirectionnelle naturelle d’une interview. Le non-engagement, le détournement, l’hésitation et l’exagération sont tous des composants essentiels dans la tension intervieweur-interviewé. En mettant la dynamique d’interview au centre de notre analyse, nous aspirons à mieux comprendre comment la dynamique d’interview structure ce qui est dit et ce qui n’est pas dit. Dans cette étude, nous abordons des composants clés de la tension d’interview et proposons un modèle de traitement de langue naturelle qui peut incorporer de façon efficace ces composants dans des interviews d’histoire orale numérisées afin d’identifier des points de tensions. Avec des expériences sur un transcrit annoté, nous vérifions l’efficacité de notre modèle. Ce modèle fournit un cadre à utiliser dans de futures recherches sur la dialogique de l’interview.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".