“Come With Me on My #Griefjourney”: First-Person Narratives of Grief on TikTok
Bibliographic record
Abstract
As scholarship on death and dying has demonstrated, the tendency to ritualize experiences with death and grief stems from the need to find ways to express the pain and agony of loss. In recent decades, these expressions have moved from offline spaces to online spaces, marking a new era of digitally mediated mourning. Social media platforms like Facebook and TikTok enable users to connect, interact, and share across geographical boundaries and time constraints. They have also become virtual memorial spaces for users to post videos, photos, and tributes to deceased loved ones. With a rapid rise in popularity and ever-expanding user base, TikTok's blend of entertainment, self-expression, and emotional connection has become a compelling force among social media platforms. While online mourning is an established phenomenon, TikTok's presence in this space is a relatively recent development; users leverage TikTok's affordances to express their grief publicly and to continue connections with deceased loved ones, in the process, creating a digital “safe space” for public displays of grief. Through the analysis of grief narratives on TikTok and comparison to grief practices on other social media platforms, such as blogs and Facebook, a platform widely associated with online mourning, this article introduces a new form of first-person grief narrative and reflects on how social grieving and memorialization practices are evolving in changing social media environments.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.022 | 0.022 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".