No Consistent Evidence for Associations Between Various Forms of Social Media Usage and Emotional Prowess: A Multi-Study Approach With Three Adult Samples
Bibliographic record
Abstract
The good and bad impacts of social media on individuals and societies remain poorly understood and highly debated. An often-discussed, yet little-studied worry about social media usage is that it may breed diminished social and emotional abilities. Here, we tested this assumption across three studies with adult samples (N = 316, 1,879, 903). We used different indicators of emotional prowess (i.e., emotional intelligence, emotion recognition), a broad set of social media usage measures and adopted a three-pronged analysis approach featuring zero-order correlations, multiple regressions, and conditional random forests. Our findings do not support consistent evidence for associations between social media usage and emotional prowess. Instead, we find conflicting evidence for passive social media usage (related to lower overall emotional intelligence but better emotion recognition) and active social media usage (related to higher overall emotional intelligence but worse emotion recognition). We find some evidence for positive associations between emotional prowess and general smartphone usage and text messaging usage. Further, we find largely inconsistent and/or null effects for social media addiction, general social media usage, general smartphone usage, video gaming, and media sharing. In the absence of consistent effects of social media usage, we find strong, robust, and replicable associations between age and emotional prowess.
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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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".