Digital Rebranding and Quotidian Self-Reinvention: Wellness Influencer-Healers’ Negotiations of Authenticity and Tellability Crises on Instagram
Bibliographic record
Abstract
This paper examines practices of digital rebranding, characterised as the strategic overhaul and staged revitalization of one’s digital persona, within wellness influencer cultures on Instagram. Processes of rebranding, which are communicatively afforded by and performatively augmented through platform tools, features and semiotic resources, introduce a narrative disjuncture that requires delicate rhetorical management of self-historicity in relation to neoteric self-presentation practices in order to preserve impressions of authenticity. Drawing on data collected during a longitudinal blended digital ethnographic study, including screen-based participant observations, social media content extraction and oral interviews, I explore the ways in which female wellness influencer-healers discursively (re)negotiate their self-positioning in connection to their current and past digital brands and, relationally, their (imagined) audiences. I suggest that rebranding practices are narratively structured by and, in turn, enact authenticity through discourses of ‘personal growth’ and ‘pedagogical self reflection,’ thereby constructing self-presentational shifts as emergent and organic evolutions in one’s online presence. The paper further argues that digital rebranding manifests from compounded crises of authenticity and tellability, compelling users to continuously recalibrate their online personas in alignment with evolving platform norms and audience expectations. The analysis therefore foregrounds the paradoxical struggle of maintaining a stable personal brand whilst adapting to the dynamic nature of social media.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".