Validity of Social Media Assessments in Personnel Selection
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.
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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 | 0.073 | 0.197 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".