El fenómeno del meme en la estrategia creativa de Netflix España en Twitter
Bibliographic record
Abstract
The objective of this study is to determine what creative use Netflix Spain makes of audience-capturing memes to promote its contents on Twitter, chosen for its public nature, broad-ranging influence and growing reach. The main objective is to evaluate whether the memes lead to a level of engagement that makes their contents go viral. The specific objectives are: to measure the level of attraction that shared memes generate compared with other resources published by Netflix Spain on its Twitter account (@NetflixES), to analyse which formats and contents result in the most retweets, likes and replies, and to identify for which communication goals the memes are used. The study uses a mixed quantitative and qualitative methodology, with a sample of 112 memes from 307 publications from the fourth quarter of 2019. Findings: memes result in the third-biggest form of attraction after emoticons and weblinks; there is no direct connection between the most frequently used formats (visual text, image macro and video clip) and the most interactive formats (graphic, collage and video clip). Memes are published with the goal of promoting the Netflix brand and its catalogue as well as to generate discussion among users. It is concluded that memes are a preferred form of communication in the creative strategy of Netflix Spain. They are a highly powerful form of attraction that creates emotional ties between the Netflix platform and its users.
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 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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".