How Social Media Shapes Our Happiness: Exploring the Mediating Effects of Social Comparison and Materialism
Bibliographic record
Abstract
Despite its widespread presence, social media’s impact on happiness remains unclear. This study investigates the intricate relationship between social media use, happiness, materialism, and social comparison within the specific context of Saudi Arabia. It examines whether materialism and social comparison mediate the association between social media use and happiness. Data were collected through surveys focusing on popular social media platforms used by Saudi adults. These surveys measured social media use, happiness levels, materialistic tendencies, and the propensity for social comparison. A sample of 250 Saudi nationals residing in Riyadh, encompassing various genders and age groups, participated in the study. Statistical analyses using advanced software (SPSS, AMOS 26, and PROCESS 4.0) were conducted to assess correlations and potential mediating effects. The findings revealed a significant positive association between increased social media use and both higher materialism and social comparison. Surprisingly, a positive correlation was also observed between social media use and reported happiness levels. Interestingly, both materialism and social comparison significantly mediated the relationship between social media use and happiness. This suggests that these factors play a crucial role in explaining the observed connection. The findings imply that content presented on social media platforms might cultivate materialistic desires and a drive for upward social comparison, ultimately impacting happiness through these indirect pathways. This research contributes to the existing body of knowledge by proposing and testing a model that links social media use with happiness directly and indirectly through the mediating roles of materialism and social comparison. The study concludes by discussing the practical implications of these findings and outlining potential directions for future research.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".