Unwavering interests in influencers: Influencer-focused barrages in social media product review videos
Bibliographic record
Abstract
Barrages, or live text comments displayed on the video interface, are valuable audience-contributed content. This research examines barrages commenting on influencers (influencer-focused barrages) and their effect on the effectiveness of product review videos. A dataset comprising 257,650 barrages in 295 popular influencer-created product review videos on Bilibili was analyzed. The results show that barrage volume positively affects the effectiveness of videos, as measured by the counts of likes and collects. Importantly, influencer-focused barrages, although comprising a small portion of the total barrage volume, exert an additional positive impact. Moreover, this effect differs across videos of various durations and influencer genders. It is more pronounced in longer videos and those featuring female influencers compared to shorter videos and those by male influencers. A content analysis further reveals differences in barrage content directed at male and female influencers. An additional experimental study further confirms the effect of influencer-focused barrage volume on video effectiveness. This research enhances both theoretical and practical understandings of barrage content and its impact on influencer videos, providing researchers, businesses, and influencers with a novel perspective and useful insights.
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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 | 0.003 | 0.002 |
| 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 | 0.001 |
| 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".