Pranks and the Viral Canon: On Top-Creator Content from YouTube to TikTok
Bibliographic record
Abstract
Since the early 2000s, digital pranks—or filmed practical jokes—have earned a reputation for platform-based popularity. But how prominent are such pranks amongst other “viral” genres? And what might they reveal, more generally, about viral video as a cultural form? In this essay, I address these broad questions from a specific perspective: I consider the digital prank in the context of two datasets of video content by top-ranked English-language creators on TikTok and YouTube, 2020-2023 and 2012-2023, respectively. I find that digital pranks have been less prominent within this context than potentially expected, but that they have also reflected more general tendencies shared across the videos collected, like fixations on visual pleasure, materiality, and experimentation. I also engage in a more methodological discussion, considering complications that emerge when addressing platform-based media from this type of interpretive and generalizing perspective. I suggest that a (digital) humanistic approach to popular digital content can expand the types of new media canons available for analysis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.000 | 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 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".