From “Being Real” to “Relatable Tales”: Formatted Authenticity and Stories in TikTok Short Form Videos
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 it