Social media “ghosts”: how Facebook (Meta) Memories complicates healing for survivors of intimate partner violence
Bibliographic record
Abstract
This paper contributes to feminist conversations about algorithms and design justice by examining ways Facebook’s (Meta) Memories affordance, when it draws on previously posted photographs of abusive former partners, is problematic for gender-based violence (GBV) survivors. With analyses drawn from semi-structured interviews with twelve “survivor-users” and a walkthrough of Memories’ settings to better understand what opportunities users have to control this function, this paper finds that Memories triggers survivors, makes their abuser seem inescapable and reduces survivors’ sense of agency, among other challenges to their well-being. By extending abusers’ intimidation back into survivors’ lives, Memories unintentionally supports perpetrators’ aims: to scare, isolate and punish their targets. This paper concludes that a masculinist bias within Memories’ design leads to painful consequences for survivor-users of varying identities. Ultimately, this study proposes possible means of addressing Memories’ challenges for survivor-users, including the option for users to opt in to, rather than out of, the function in the first place; alterations to Memories’ interface to enable the immediate flagging of problematic content; and continued movements towards trauma-informed design practices in the technology sector.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| 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".