To engage or not engage? The features of video content on <scp>YouTube</scp> affecting digital consumer engagement
Bibliographic record
Abstract
Abstract Popularity on YouTube is an important metric for influencers and brands. It is linked to video relevance, content, and features that attract audience attention and interest. We present and test a model of YouTube video popularity drivers that trigger several engagement actions (i.e., number of views, likes, dislikes, and comments). These drivers include characteristics—such as language elements, linguistic style, subjectivity, emotion valence, and video category—that influence online video popularity on YouTube. An analysis of a database comprising more than 11,000 videos from 150 digital influencers shows that several factors help to boost the number of views, likes/dislikes, and comments. We find that medium‐length and long videos posted during non‐business hours and weekdays and those using a subjective language style, less‐active events, and temporal indications are more likely to receive views, likes, and comments. Moreover, the use of negative or low‐arousal emotion helps to promote a general interest in a YouTube video.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".