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Trust has emerged as a prevalent construct to describe relationships between people and between people and technology in myriad domains. Across disciplines, researchers have relied on many different questionnaires to measure trust. The degree to which these questionnaires differ has not been systematically explored. In this paper, we use a word-embedding text analysis technique to identify the differences and common themes across the most used trust questionnaires and provide guidelines for questionnaire selection. A mapping review was conducted to identify the existing trust questionnaires. In total, we included 46 trust questionnaires from three main domains (i.e., Automation, Humans, and E-commerce) with a total of 626 items measuring different trust layers (i.e., Dispositional, Learned, and Situational). Next, we encoded the words within each questionnaire using GloVe word embeddings and computed the embedding for each questionnaire item, and for each questionnaire. We reduced the dimensionality of the resulting dataset using UMAP to visualize these embeddings in scatterplots and implemented the visualization in a web app for interactive exploration of the questionnaires (link to app). At the word level, the semantic space serves to produce a lexicon of trust-related words. At the item and questionnaire level, the analysis provided recommendation on questionnaire selection based on the dispersion of questionnaires’ items and at the domain and layer composition of each questionnaire. Along with the web app, the results help explore the semantic space of trust questionnaires and guide the questionnaire selection process. The results provide a novel means to compare and select trust questionnaires and to glean insights about trust from spoken dialog or written comments.

Objective The objective of this study was to estimate trust from conversations using both lexical and acoustic data. Background As NASA moves to long-duration space exploration operations, the increasing need for cooperation between humans and virtual agents requires real-time trust estimation by virtual agents. Measuring trust through conversation is a novel and unintrusive approach. Method A 2 (reliability) × 2 (cycles) × 3 (events) within-subject study with habitat system maintenance was designed to elicit various levels of trust in a conversational agent. Participants had trust-related conversations with the conversational agent at the end of each decision-making task. To estimate trust, subjective trust ratings were predicted using machine learning models trained on three types of conversational features (i.e., lexical, acoustic, and combined). After training, model explanation was performed using variable importance and partial dependence plots. Results Results showed that a random forest algorithm, trained using the combined lexical and acoustic features, predicted trust in the conversational agent most accurately [Formula: see text]. The most important predictors were a combination of lexical and acoustic cues: average sentiment considering valence shifters, the mean of formants, and Mel-frequency cepstral coefficients (MFCC). These conversational features were identified as partial mediators predicting people’s trust. Conclusion Precise trust estimation from conversation requires lexical cues and acoustic cues. Application These results showed the possibility of using conversational data to measure trust, and potentially other dynamic mental states, unobtrusively and dynamically.