Social media observation of ex-partners is associated with greater breakup distress, negative affect, and jealousy
Bibliographic record
Abstract
After a romantic breakup, many people observe ex-partners on social media. However, it remains unclear whether observation has downstream consequences for breakup recovery, whether it matters if the observation is active (intentional) or passive (unintentional), and whether attachment anxiety plays a moderating role. The present studies ( N = 762) used longitudinal, experimental, and daily diary methods to clarify our understanding. In Study 1, active observation on Facebook predicted heightened breakup distress within three months of a breakup and six months later, especially for people higher in anxious attachment. Study 2 found that experimentally enhancing the salience of observation increased negative affect and jealousy. In Studies 3 and 4, active observation on sites like Instagram and Snapchat was associated with greater same-day and next-day breakup distress, whereas passive observation was associated with greater same-day negative affect. Overall, these results suggest that reducing social media observation may assist breakup recovery. • Four studies examined how observing exes on social media impacts breakup recovery. • These studies used longitudinal, experimental, and daily diary methods. • Active/intentional and passive/unintentional observation predicted worse recovery. • Anxious attachment often amplified the impact of observation on recovery. • These results suggest unfriending/unfollowing/muting ex-partners on social media.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".