Virtual Mourning and Memory Construction on Facebook
Bibliographic record
Abstract
This article investigates the online information practices of persons grieving and mourning via Facebook. It examines how, or whether, these practices and Facebook’s terms of use policies have implications for the bereaved and/or the memory of the deceased. To explore these questions, we compared traditional publicly recorded asynchronous modes of grieving (i.e., obituaries) with Facebook’s asynchronous features (i.e., pages, photos, messages, profiles, comments). Additionally, by applying observational techniques to Facebook memorial pages and Facebook profiles, conducting a survey, and interviewing respondents as a follow-up to the survey, we examined the benefits of and issues surrounding online information sharing via Facebook when coping with the loss of another. We found that the immediacy of publishing comments, messages, wall posts, and photos provides Facebook mourners with a quick outlet for their emotions and a means of timely group support; however, these actions directly affect the online curation of the deceased’s self and memory and also create an environment of competition among mourners. The aforementioned benefits and complications of using Facebook during bereavement are shaped by the policies outlined by the social media platform.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.005 |
| 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".