Release of the Fourth Season of Money Heist: Analysis of Its Social Audience on Twitter during Lockdown in Spain
Bibliographic record
Abstract
Nowadays we are witnessing a significant change in content consumption. This, together with the global health situation, has caused some behaviors to accelerate. This research focuses on the specific case of the lockdown in Spain and the coincidence with the launch of the fourth season of Money Heist compared to the launch of season three. Starting with a review of the theoretical framework, in which the related concepts of coronavirus, television, and Video on Demand (VOD) platforms are presented, the importance of transmedia communication is also introduced. The methodological aspect is developed through content analysis and in-depth interviews. The tool used on the first methodology has been Twlets. With regard to the sources, the specific bibliography of the audiovisual sector, the official profile of the series on Twitter and personal interviews with professionals from the communication department of the production company, Vancouver Media, and from the series directing were taken into account. The methodology used to carry out this work has been the analysis of quantitative–qualitative content of the various sources consulted. The results of the study are presented in graphs, crossing the data from the different sources to detect the strategies of marketing and communication used for the release of the fourth season of the series. These results reflect the change in the communication strategy, the behavior of the social audience of the Twitter account of Money Heist (La Casa de Papel) and its relationship with the period of lockdown in Spain.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".