Estimating Personality from Social Media Posts
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
An individual's personality determines the probable repertoire of their reactions to a particular situation. A social robot is much more effective if it is able to learn and so take into account the properties of the humans around it, including personalities. We investigate how well personality can be estimated based on modest amounts of speech or writing, which a social robot might (over)hear. Such a technique also permits humans to be able to infer the personalities of other humans 'at a distance' based on their writing in political, hiring, negotiation, and other relationship settings. We design and implement a technique for predicting personality from small amounts of text, with accuracies comparable to inter-human agreement and substantially better than previous algorithmic approaches (except for a few that use much richer data). The technique works for both of the popular personality typologies, the Big Five and the Myers-Briggs. Because the approach does not require a lexicon, it is language independent. We illustrate using eight different languages, including Arabic.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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