Gender Differences in Facebook Addiction as a Coping Response to Social Stressors and Poor Self-Confidence
Bibliographic record
Abstract
The Threat Appraisal and Coping Theory suggests that when individuals perceive social stressors from important interpersonal relationships (family, friends, romance), and when they have poor self-confidence, they may display the coping behavior of seeking social support, including that provided by social media platforms such as Facebook. However, individuals who perceive intense social stressors and have poor self-confidence may use Facebook to the extent that it interferes with other areas of their lives. The present study examined this cognitive sequence that could lead to such Facebook addiction: SOCIAL STRESSORS à POOR SELF-CONFIDENCE à EXCESSIVE FACEBOOK. Because of past research showing gender differences in each of these variables, we hypothesized that women would be more likely to show the proposed cognitive sequence leading to Facebook addiction. Participants were 243 women and 209 men from a paid online Survey Monkey sample who reported demographics, three social stressors (family, friends, romance), self-confidence with Rosenberg’s Self-Esteem Scale, and excessive Facebook use with the Bergen Facebook Addiction Scale. Unlike our hypothesized results, moderated mediational analyses with 5000 bootstrapped samples found significantly higher indirect effect sizes for the three-variable sequence in men than in women, specifically when the social stressor was from family or romantic partners. One interpretation would be that when conflicts occur in intimate personal relationships (family, romance), women may have a wider network of real-life relationships in which they share their emotional concerns, whereas men are more likely to rely on online social media to vent concerns about intimate relationships.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".