Validity of Social Media Assessments in Personnel Selection
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
Abstract: Approximately two out of three recruiters report screening candidates’ KSAOs (knowledge, skills, abilities, and other characteristics like personality) or hireability based on social media platforms (e.g., Facebook, LinkedIn), often referred to as cybervetting. However, various researchers cautioned against engaging in this emerging practice due to questions about the validity of social media assessments. Therefore, we conducted a systematic review to summarize initial research on the psychometric properties of social media assessments: Reliability, construct-related validity, and criterion-related validity. Our literature search yielded 12 studies with 536 raters and 2,019 ratees, and most of these studies addressed personality traits. We found that single-rater reliability of social media assessments was mostly poor; convergent validity regarding personality traits was adequate, and criterion-related validity for job-related outcomes was small or close to zero. Convergent validity tended to be higher for ratings of extraversion and lower for neuroticism. However, given that evidence was scarce, we highlight that substantial gaps in the current state of knowledge about social media assessments remain. Thus, we conclude by discussing various avenues for future research to better understand and improve their validity.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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