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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it