Disconnect between intentions and outcomes: A comparison of regretted text and photo social networking site posts
Bibliographic record
Abstract
Many social networking site (SNS) users regret previous posts and post sensitive content despite the potential for negative consequences. Limited research has examined regret among SNS users, and it is unclear whether regret differs between text and graphic formats. An online survey of Australian SNS users (N = 995), compared regretted text to photo and video posts by examining demographic characteristics, psychological antecedents, post content, and consequences of posting. Feelings of regret were similar; however, regretted photo/video posts reported were related to a positive mood when posting, social motivations, and most frequently resulted in personal consequences (e.g., embarrassment). In comparison, regretted text posts were motivated by negative mood states and were more likely to result in social consequences. There might be a disconnection between what users hope to convey and how posts are perceived. SNS design that prompts users to consider the impacts of posts and to screen for offending content may reduce post regret. Interventions should encourage mindfulness of posting when upset and gaining self‐validation externally from SNS.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".