When private self-compassion goes public: effects of social media self-disclosure
Bibliographic record
Abstract
Both practicing self-compassion and sharing difficult experiences with trusted others to feel less alone in suffering can help individuals decrease their negative affect (Masaviru, 2016; Neff, 2003). When completed privately, self-compassion exercises can partly reduce negative affect following a shame-recall exercise, and self-disclosure literature suggests that sharing private information with a select few can benefit the individual. Social media’s popularity has brought together public disclosures, and discussions of difficult experiences. Contrary to the traditional use of social media in which users seek approval from their audience (Pinkerton et al., 2017; Sheldon & Newman, 2019), social media users are beginning to share posts about their imperfections while being self-compassionate. This research examined whether it is beneficial for university students to use social media to publicly engage in typically private practices of self-compassion. I hypothesized that those who wrote about difficult experiences with self-compassion in a public manner would experience higher negative affect, less emotional relief, and greater desire for reassurance than those who wrote in this way privately. To test this hypothesis, in Study 1 I developed and evaluated an embarrassment-recall task because sharing of embarrassment can be especially helpful in reducing this uncomfortable emotion (Leary et al., 1996). Participants in Study 1 rated their affect before and after an embarrassment-recall and a self-compassion induction. Study 2 included two independent projects: Study 2a used online questionnaires to examine correlations of participant traits and social media behaviours, while Study 2b used the embarrassment task from Study 1 to consider how variations in the expectations of privacy (private vs. public) for self-compassionate writing affected how participants evaluated their self-compassionate writing and whether they endorsed hopes related to reassurance-seeking. Participants imagined re-reading or sharing their writing on social media and then rated their hopes and affect. Study 1 demonstrated that the novel embarrassment recall task performed comparably to the existing shame recall tasks in that negative affect reduced at a similar rate for both types of recall following a self-compassion induction, forming the basis for Study 2. Study 2a demonstrated that higher trait self-compassion and lower reassurance-seeking were related to social media posting behaviour. Study 2b demonstrated that participants who imagined posting their writing to their social media had few hopes for their writing, whereas those who wrote privately were hopeful that their writing would benefit them in multiple ways. The discussion considers how the results help to broaden our understanding of social media, self-compassion, and provide new experimental methods for advancing self-compassion research.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".