Bridging in-task emotional responses with post-task evaluations in digital library search interface user studies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Interactive information retrieval (IIR) interfaces are commonly evaluated using questionnaires that collect post-task subjective measures such as satisfaction, ease of use, usefulness, and user engagement. Although the importance of measuring emotional responses during the search process has been recognized, incorporating this aspect into IIR user studies has been challenging. We have developed a novel method to capture real-time emotional responses based on advances in facial emotion classification approaches. We utilize consumer-grade front-facing cameras to collect emotional responses, which synchronize with the user’s interactions with the search interface. In a controlled laboratory study, the relevance of search results was manipulated to validate the approach’s effectiveness and explore how search results’ relevance impacts users’ emotional responses, post-task evaluations of the search interface, and interactions with search interface features. This enabled us to examine whether we could detect emotional responses, whether recency effects were observed in post-task evaluations, and whether feature use correlated with emotional responses. The study was conducted in the context of exploratory search within an academic digital library. The results of this study demonstrate that both positive and negative emotional responses can be reliably detected during the search process. There is evidence of recency effects in post-task measures, and the study identifies specific interactive features used during the experience of positive and negative emotional responses. This serves as a foundation for the use of emotional responses to supplement post-task survey data when evaluating search interfaces. • Real-time emotion detection in IIR interfaces. • Recency effects observed in post-task subjective measures. • Correlation between emotional responses and post-task evaluations. • Emotional responses variation across search interface features. • Emotions in exploratory search within academic digital libraries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.015 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it