Computer-Mediated Communication and Well-Being in the Age of Social Media: A Systematic Review
Bibliographic record
Abstract
The association between computer-mediated communication (CMC) and well-being is a complex, consequential, and hotly debated topic that has received significant attention from pundits, researchers, and the media. Conflicting research findings and fear over negative outcomes have spurred both moral panic and further research into these associations. To create a more comprehensive picture of trends, explanations, and future directions in this domain of research, we conducted a systematic meso-level review of 366 studies across 349 articles published since 2007 that report associations between CMC and well-being. Although most of this research is not explicitly theoretical, several potential theoretical mechanisms for positive and negative effects of CMC on well-being are utilized. The heterogeneity of effects in the studies we reviewed could be explained by the discipline in which the research is conducted, the methodology used, the types of CMC and well-being examined, and the population studied. Our evaluation of this body of research highlights the importance of attending to how we conceptualize communication and well-being, the questions we ask, and the populations and contexts we study when both reading and producing research on CMC and well-being.
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.
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 | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".