“Get Off My Internets”: How Anti-Fans Deconstruct Lifestyle Bloggers’ Authenticity Work
Bibliographic record
Abstract
This paper examines the nature of authenticity labour in personal lifestyle blogging through a case study of travel bloggers. Specifically, it looks at how participants in the blogging anti-fan community Get Off My Internets (GOMI) identify and deconstruct lifestyle bloggers’ efforts to perform an ‘authentic’ persona. Within the broader context of online micro-celebrity, self-branding, and persona, I examine authenticity as a kind of labour that is necessary for lifestyle blogging ‘success,’ where success is measured by metrics like heavy website traffic and brand sponsorships. Lifestyle bloggers perform authenticity partly by narrating the process of cultivating personal authenticity through the ongoing process of selfimprovement towards an idealized goal. This personal authenticity is based on existentialist notions of ‘being true’ to one’s essential nature and personal commitments. In the GOMI community, bloggers’ representations of the inner life are frequently viewed with suspicion, and interpreted as ‘staged,’ and therefore inauthentic, performances of authenticity. Bloggers are also expected to demonstrate a commitment to ethical authenticity, and, subsequently, attempts to monetize their content through sponsorships and affiliate links are viewed with suspicion. Lastly, authenticity work in lifestyle blogging involves emphasizing one’s ordinariness alongside one’s extraordinariness, resulting in what I call ‘aspirational extra/ordinariness.’ By observing trends in how travel bloggers perform authenticity and how anti-fans deconstruct these performances, it becomes apparent that critical publics identify inauthenticity in moments where the constructedness or performedness of authenticity is most apparent, indicating that while micro-celebrities rely on authenticity labour for their popularity, this very labour can detract from a persona’s perceived authenticity when it becomes obvious to publics.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.018 | 0.033 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| 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".