From “Being Real” to “Relatable Tales”: Formatted Authenticity and Stories in TikTok Short Form Videos
Bibliographic record
Abstract
Authenticity in the sense of off-the-cuff, raw, believable presentation of the teller and their everyday life through storytelling has been a widely circulating discourse in digital storytelling (e.g. brand storytelling). In my longitudinal technographic study of stories online, I have explored the connections of this type of authenticity with stories as a feature on platforms (Georgakopoulou 2022). I have shown authenticity to be a platformed directive, supported by specific affordances and design, and integrally connected with the storytelling practice of sharing-life-in-the-moment. These choices have developed recognizability and normativity (i.e. formatting). Building on this research, in this article, I examine how formatted authenticity in stories migrates onto TikTok short form videos. I focus on a series of videos with conventionalized captions “when your/my mum …” that build a generic tale about roles and relationships within the family. Using small stories and positioning analysis, I show how the formatted authenticity that I have attested to in previous work is reconfigured and repurposed at different levels, in line with TikTok affordances for creating multi-layered, intertextual storytelling. The intermingling of the personal with the collective/generic within sharing-life-in-the moment emphasizes the shift of authenticity from teller-based truth-telling to a tale-based relatability. The discussion shows how studying authenticity in social media narratives requires a historical, media-genealogical approach so as to understand the evolution and trans-platformization of storytelling genres and choices that serve as recognizable emblems of authenticity.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".