Socially Interactive and Passive Technologies Enhance Friendship Quality: An Investigation of the Mediating Roles of Online and Offline Self-Disclosure
Bibliographic record
Abstract
Previous studies indicate that characteristics of social-based technologies (STs) stimulate the sharing of intimate information online, which in turn enhances the quality of friendships. In addition, intimate online self-disclosure has been positively associated with offline self-disclosure. One objective of the current study was to combine the literature and test a model which postulates that STs use stimulates online self-disclosure which facilitates offline self-disclosure and, thereby, enhances the quality of close friendships. A second objective of this study was to examine if the aforementioned model applies to two categories of STs, including socially interactive technologies (SITs; e.g., instant messaging) and socially passive technologies (SPTs; e.g., reading posts on social networking sites). An online survey was conducted with 212 young adults between 18 and 25 years of age. The proposed indirect positive effects of SITs and SPTs use on the quality of friendships were supported. The positive effect of SITs use on the quality of friendships was explained entirely by the young adults' disclosure of personal information when using SITs which facilitated intimate self-disclosure during face-to-face interactions. Although there was not a direct effect of SPTs use on the quality of friendships, SPTs use was positively related to SPTs self-disclosure, which had a positive effect on offline self-disclosure. The current study enhances our understanding regarding the positive effects associated with the use of STs among close friends and identifies the contribution of online self-disclosure for offline interactions.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".