Self-Regulation of Internet Behaviors on Social Media Platforms
Bibliographic record
Abstract
The current research sought a comprehensive understanding about the consequences of information-sharing behavior on social media, given public concerns about privacy violations. We used a mixed-methods approach to investigate the influence of the self on “revealing” and emotional “healing” experiences online. Respondents completed a survey measuring sense of self and motivations for using social media, as well as revealing and healing attitudes and behavior. We conducted a principal component factor analysis on separate parts of the survey and ran Pearson correlations of the emerging factors. Qualitative data describing experiences of online self-disclosure were used to illustrate the correlational findings. The “revealing” factors contrasted adaptive with maladaptive and naïve posting. The sense of self, as well as motivations for social media use, influenced whether users engaged in destructive posting behaviors. The “healing” factors were associated with positive motivations for self-disclosure, seeking a supportive online community, and building resilience. Correlational data revealed that respondents with an insecure or asocial sense of self felt the greater need for online self-disclosure. Motivations to self-disclose online and experiences of “healing”, with the help of a supportive online community, depended on whether the sense of self was secure, insecure, or asocial.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".