Social Media Reactions to Festival Cancellation Announcements
Bibliographic record
Abstract
There are many reasons organizations cancel a festival. Regardless of the rationale, the organization's reputation can be preserved by communicating this crucial message in an appropriate way and by understanding people's perception of a cancellation announcement. The purpose of this research is to find out how festival administrators communicate a festival cancellation on social media and how the attendees, who will ultimately determine the success or failure of a festival, react to this message. Between January–June 2018, we collected 47 festival cancellation messages on Facebook and the 8,886 replies to these messages. We undertook a content analysis of both the cancellation message and the comments on the Facebook cancellation post. We found that most of the organizers used a primary response strategy, characterized by accepting blame to communicate the cancellation of the festival. This kind of response has a significant positive association with the comments characterized by building relationships. The sentiments in the cancellation posts were mainly shame and sadness and the comments on these posts were most often negative with sad and disgusted sentiments. Findings were somewhat consistent with past research and recommendations provide insight for further theoretical development.
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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 | 0.003 | 0.016 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".