An Advanced Forecasting Model Leveraging Emotion‐Gesture Correlation to Predict Returning Visitors Surpasses Visit Duration as a Predictive Factor
Bibliographic record
Abstract
This research paper explores the effectiveness of user emotional experience as a predictor for future returning of the user to the website. Traditional web analytics have been limited in their ability to accurately capture the nuances of user experiences. Methods like eye tracking, speech tracking, and surveys, while insightful, often suffer from being overly intrusive, leading to biased results. This study introduces a state of the art, nonintrusive method of measuring user experience: touch‐gesture based emotion measurement. This technique leverages the subconscious nature of touch gestures to gather emotional data, allowing for a more authentic and unbiased user interaction with websites. To leverage this method, we first gained explicit data collection consent and gathered browsing data from 164,527 users across a 1‐year period on a Canadian e‐commerce website visited from a touchscreen device. While the sample size is significantly large, the sample is primarily made up of visitors from Canada, which could limit generalization of the findings. Using this data, we implemented an AI model which predicts whether a user is likely to return to the website or not, primarily based on their emotional touch gestures on their first visit with an accuracy of 91.7%. This approach not only enhances our understanding of user engagement but also opens new avenues for optimizing user experience in untested digital spaces such as e‐learning and mental health.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".