Unlocking Insights: An Interface for Understanding Dialogue Data

Many lines of human factors inquiry rely on dialogue data to examine team dynamics, coordination, and trust in automation. While embeddings enable the transformation of such qualitative information into mathematical representations, they can be challenging to explore—a vector of hundreds or thousands of numbers lacks simple interpretation. To address this, we introduce an interactive interface using RShiny, facilitating the exploration of large datasets containing rich affective context, including lexical, and acoustic information linked to experimental outcomes. Motivated by research quantifying trust in automation during the NASA HERA Study, we aim to pinpoint crucial conversation moments to better understand how social dynamics influence individuals’ development of trust in automation. We anticipate that our method will enhance construct transparency for researchers studying dialogue data.