AWS-EP: A Multi-Task Prediction Approach for MBTI/Big5 Personality Tests
Bibliographic record
Abstract
Personality and preferences are essential variables in computational sociology and social science. They describe differences between people at both individual and group levels. In recent years, automated approaches that detect personality traits have received much attention due to the massive availability of individuals' digital footprints. Furthermore, researchers have demonstrated a strong link between personality traits and various downstream tasks such as personalized filtering, profile categorization, and profile embedding. Therefore, the detection of individuals' preferences has become a critical process for improving the performance of different tasks. In this paper, we build on the importance of the individual's behaviour and propose a novel multitask modeling approach that understands and models the users' personalities based on their textual posts and comments within a multimedia framework. The novelties of our work compared to state-of-the-art personality prediction models are: improving the performance of the Big five-factor model (Big5) personality test using shared information from the Myers Briggs Type Indicator (MBTI) test, and proposing a one personality detection framework that accurately predicts both MBTI and Big5 tests simultaneously. Predicting both tests simultaneously improves the personality detection framework's flexibility to be used for different goals instead of being used only for a unique purpose (whether for the MBTI test or for the Big5 test separately). Experiments and results demonstrate that our solution outperforms state-of-the-art models across multiple famous personality datasets.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".