Social media activity in a festival context: temporal and content analysis
Bibliographic record
Abstract
Purpose This study explores the multi-phasic experience of festivals to understand the nature, purpose and degree of social media (SM) use before, during and after festival occurrence and how this may inform better engagement of attendees. Design/methodology/approach A census of tweets and posts from four festivals’ Twitter handles and Facebook accounts were coded and analyzed across three time points: one week prior, during and one week after the festival. They were coded on nature (e.g. conversational, promotional, informational), purpose (e.g. information-seeking, friendship/relationship) and presence of links, photos, etc. Tests for platform influences on usage were conducted. Findings In total, 1,169 tweets and 483 posts were captured. Two-thirds of SM activity occurred during the festivals, one-third pre-festival and minimal activity post festival. Temporal analyses found that while the purpose and nature of the message content varied across festival time points, this was often dependent on SM platform. Research limitations/implications Festivals are not taking advantage of the multi-phase experience model and the utility of SM to maintain contact and encourage visitors to continue processing their experience after the festival. This lost opportunity has implications for re-patronizing behaviour and sponsor relationships. Originality value Leung et al. (2013a) call for sector specific research to elucidate SM use in tourism. Festivals provide a unique environment of co-created experience. Findings suggest differential usage of SM across festival time frames and platforms that can be used to guide festival organizations’ SM communication to better engage its patrons.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".