Netflix's communication strategy on Twitter and Instagram during the unlock in Spain: humour, proximity and information
Bibliographic record
Abstract
The Covid-19 pandemic and the arrival of Disney + marked the second quarter of 2020 in the Spanish audiovisual market. Thus, the period of home confinement among the Spanish population coincided with the irruption of the new streaming service of one of the best-known and most loved brands worldwide. However, Netflix was the most consumed SVoD during this period. The objective of this research is to find out what the Californian company has done in communicative terms as a market leader and in the face of the need to adapt to the new circumstances of its audiences. The results show how Netflix Spain has integrated COVID-19 in its social media strategy in the pass between the lockdown and maximum consumption to a progressive lessening of social restrictions. The content analysis of Twitter and Instagram found 121 messages regarding pandemic (from a total of 1380). Netflix employed Twitter to connect with its audiences with humor, proximity and information, using taboos in the hardest moments, and an increased frequency of publications as the health situation improved. On the contrary, on Instagram there was no specific strategy, but imitation of the practices on Twitter and scarce references to COVID. Besides, there has been an evolution of the messages more or less parallel to the public health changes, choosing a strategy of proximity with the users, and with a communication closer to an influencer rather than a company.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 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".