Are You Going to Delete Me? Latent Profiles of Post-Relationship Breakup Social Media Use and Emotional Distress
Bibliographic record
Abstract
When a relationship ends, former partners must make decisions about their online, often public, connections and history, which involve a complex disentangling process. We examined post-breakup behaviors including monitoring, interacting, deleting posts/photos, deleting the former partner, deleting the partner's family/friends, stopping social media (SM) use, and keeping digital possessions. Participants (N = 256) who had experienced a breakup within the last year completed an online survey. Approximately 38 percent reported experiencing distress over the breakup sometimes or more often. Utilizing latent profile analysis, we identified four latent classes (or profiles) of breakup SM behaviors; we also examined associations between the class and breakup emotional distress. Most participants were clean breakers (61.3 percent), who did zero to very little monitoring, interacting, or deleting and were unlikely to delete their ex-partner, stop use, or keep digital possessions. Wistful reminiscers (12.9 percent) were similar to clean breakers in terms of engaging in very little of any deleting behaviors, stopping use, or keeping digital possessions; however, they engaged in frequent amounts of monitoring their ex-partner as well as interacting with their ex-partner and their ex-partner's family/friends. Ritual cleansers (15.6 percent) were similar to clean breakers in terms of engaging in very little to no monitoring and interacting; however, they engaged in deleting their SM history, their ex-partner's family/friends, and their ex-partner. Impulsives (10.2 percent) engaged in high amounts of all the SM behaviors. In terms of emotional distress, impulsives showed the highest levels of distress, followed by wistful reminiscers, ritual cleansers, and then clean breakers.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".