The Impact of Social Networking Site on Social Well-Being During the Pandemic
Bibliographic record
Abstract
This research examines the impact of social distancing on social well being and academic performance during the COVID-19 pandemic and how social networking sites (SNS) may moderate this relationship. Social distancing has been implemented globally to prevent the spread of the Coronavirus, leading to temporary closures of educational institutions and social networks, causing negative psychological effects such as distress, tediousness, and loneliness. This study hypothesizes that social distancing negatively affects social well-being, and social well-being positively affects academic performance. Furthermore, we suggest that SNS use may moderate the relationship between social distancing and social well-being, weakening the negative effect; to do so, the current study develops a research model with three hypotheses, emphasizing the impact of social distancing and SNS use on social well-being and academic performance during the pandemic. To test our research model, 103 college students were surveyed. Partial least squares (PLS) structural equation modeling was employed to analyze our data, and these analyses provided empirical support for the proposed hypotheses. We believe our model extends our knowledge of (1) the traditional theories related to SNS use and social well-being, (2) the impact of SNS use on academic performance, and (3) moderator and mediator in the relationships between SNS use, social well-being, and academic performance.
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".