ISCA Archive Interspeech 2021
ISCA Archive Interspeech 2021

A Psychology-Driven Computational Analysis of Political Interviews

Darren Cook, Miri Zilka, Simon Maskell, Laurence Alison

Can an interviewer influence the cooperativeness of an interviewee? The role of an interviewer in actualising a successful interview is an active field of social psychological research. A large-scale analysis of interviews, however, typically involves time-exorbitant manual tasks and considerable human effort. Despite recent advances in computational fields, many automated methods continue to rely on manually labelled training data to establish ground-truth. This reliance obscures explainability and hinders the mobility of analysis between applications. In this work, we introduce a cross-disciplinary approach to analysing interviewer efficacy. We suggest computational success measures as a transparent, automated, and reproducible alternative for pre-labelled data. We validate these measures with a small-scale study with human-responders. To study the interviewer’s influence on the interviewee we utilise features informed by social psychological theory to predict interview quality based on the interviewer’s linguistic behaviour. Our psychologically informed model significantly outperforms a bag-of-words model, demonstrating the strength of a cross-disciplinary approach toward the analysis of conversational data at scale.

doi: 10.21437/Interspeech.2021-2249

Cite as: Cook, D., Zilka, M., Maskell, S., Alison, L. (2021) A Psychology-Driven Computational Analysis of Political Interviews. Proc. Interspeech 2021, 1942-1946, doi: 10.21437/Interspeech.2021-2249

  author={Darren Cook and Miri Zilka and Simon Maskell and Laurence Alison},
  title={{A Psychology-Driven Computational Analysis of Political Interviews}},
  booktitle={Proc. Interspeech 2021},