What does the media tell us about a destination? The Cancun case, seen from the USA, Canada, and Mexico
Bibliographic record
Abstract
Purpose This study aims to present a framework for automatically collecting, cleaning and analyzing text (news articles, in this case) to provide valuable decision-making information to destination management organizations. Keeping a record of certain aspects of the projected destination image of an attraction (Cancun in this study) will grant the design of better strategies for the promotion and administration of destinations without the time-consuming effort of manually evaluating high quantities of textual information. Design/methodology/approach Using Web scraping, news articles were collected from the USA, Mexico and Canada over an interval of one year. The documents were analyzed using an automatic topic modeling method known as Latent Dirichlet Allocation and a coherence analysis to determine the number of themes present in each collection. With the data provided, the authors were able to extract valuable information to understand how Cancun is presented to the countries. Findings It was found that in all countries, Cancun is an important destination to travel and vacation; however, given the period defined for this study (from July 2021 to July 2022), an important part of the articles analyzed was concerned with the sanitary measures derived from the COVID-19 pandemic. Besides, given the rise of violence and the threat of organized crime, many articles from the three countries are focused on warning potential tourists about the risks of traveling to Cancun. Originality/value The examination of the relevant literature revealed that similar analyses are manually performed by the experts on a set of predefined categories. Although those approaches are methodologically sound, the logistic effort and the time used could become prohibitively expensive, precluding carrying out this analysis frequently. Additionally, the preestablished categories to be studied in press articles may distort the results. For these reasons, the proposed framework automatically allows for gathering valuable information for decision-making in an unbiased manner.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".