Measuring destination image of an Italian island: An analysis of online content generated by local operators and tourists
Bibliographic record
Abstract
The understanding of destination image is a key point for tourism enterprises, local authorities, and policy makers. This study explores the case of Capraia, a small island located in Tuscany, to analyze how tourists (tourism demand) and local operators (tourism supply) create and communicate the island’s online image. This research quantitatively examines online communication on the two sides of the tourism market to monitor the online destination image of the island of Capraia. To build on previous research in this area, this study adopts a web content mining approach to assess the characteristics of content published online. The main dimensions of destination image (as developed in the literature) are used as a basis to create a dictionary for automated content analysis. A total of 24 tourism promotion websites and 9,180 tourist Instagram posts were analyzed. Findings reveal discrepancies between the image proposed by local operators and that perceived by tourists. Local operators mostly communicate general information to discover the destination, while tourists prioritize communication based on emotional appeal and personal experience on the island. This research aims to provide support for local operators and policy makers in decisions relating to communication and in defining the island image.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".