“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 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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".