The Effects of Self-Control and Self-Awareness on Social Media Usage, Self-Esteem, and Affect
Bibliographic record
Abstract
Background: With the increase in social media usage due to the COVID-19 pandemic, investigation into factors that mitigate excessive and problematic usage is warranted. Factors such as self-awareness were included in the analysis of social media usage as it leads individuals to focus on personal ideal standards, begging the question as to whether high self-awareness limits problematic social media usage. Self-control, strengthened by self-awareness, was measured to examine its involvement in limiting excessive social media usage. Self-esteem and affect were included in analyses as they have never been examined in relation to both self-awareness and social media usage. It was hypothesized that self-awareness would be negatively related to social media usage, given self-control levels are high. Furthermore, self-awareness would be positively related to self-control, self-esteem, and affect, given social media usage is low. Methods: 125 psychology students (73.6% female) completed scales on self-awareness, social media usage, self-esteem, self-control, and affect. Linear regressions with moderation and mediation were conducted. Results: No moderation occurred but it was found that self-control mediated the relationship between self-awareness and social media usage. Self-awareness was positively related to self-esteem, self-control, and positive affect. Social media usage was not significantly related to self-esteem, positive affect, or negative affect. Self-control acted as a mediator in numerous analyses involving self-awareness and social media usage. Conclusions: Self-awareness promotes self-control, resulting in reduced social media usage. Future research should focus on cultivating self-awareness and the consequent self-control to help avoid the negative outcomes associated with social media usage (e.g., reduced self-esteem).
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".