The socially poor get richer, the rich get poorer: The effect of online self-disclosure on social connectedness and well-being is conditional on social anxiety and audience size
Bibliographic record
Abstract
Self-disclosure taking place in computer-mediated communication (CMC) is generally associated with enhanced well-being because it evokes a greater sense of connectedness. It has been established that the magnitude of the benefits reaped from online self-disclosure is conditional on social anxiety (under the lens of the poor-get-richer vs. rich-get-richer hypotheses) or audience size. What remains to be understood is whether those with low (compared to high) social anxiety experience greater social connectedness and subjective well-being in dyadic and/or masspersonal CMC. A sample of 411 Canadian undergraduate students (aged 17–21 years old) self-reported their anxiety in social situations, online self-disclosure in dyadic and masspersonal communication, current feelings of social connectedness, and subjective well-being. Model 7 of the PROCESS macro for SPSS was used to test the indirect effect of online self-disclosure on subjective well-being through feelings of social connectedness, conditioned on values of social anxiety. The model was run separately for dyadic and masspersonal CMC. Online self-disclosure was associated with positive outcomes only for those with high social anxiety. In both contexts, online self-disclosure was associated with enhanced social connectedness and in turn more positive subjective well-being. In contrast, for those with low social anxiety, increases in self-disclosure in masspersonal CMC was associated with decreases in social connectedness and poorer well-being. The indirect effect was not significant for dyadic CMC. Overall, the findings contribute to a more informed understanding of online self-disclosure as a double-edged sword. Theoretical implications for the poor-get-richer and rich-get-richer perspectives are discussed.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".