A Study on Cross-Cultural Analysis of Social Media Data and Leisure Travel Preference Prediction Supported by Cluster Analysis Algorithm
Bibliographic record
Abstract
In this paper, leisure tourism is taken as the entry point of the research, and the fused location key point features are added and integrated with the multidimensional features of time, location and space to construct an accurate portrait of social media tourism users.On the basis of tourism user profiles, a two-step clustering algorithm is combined to carry out cross-cultural analysis of social media data, to explore and excavate the performance of users' tourism preferences under the cross-cultural ability of social media.Meanwhile, in order to realize the prediction of leisure tourism preference, a combined model based on BP neural network and ARIMA is proposed to improve the accuracy of leisure tourism preference prediction by fully considering the linear and nonlinear laws of tourism statistics.The ARIMA-BP combination prediction model is applied to predict the leisure tourism preference in the future from 2027-2034.During the period 2027-2029, the number of leisure tourism tourists maintains a high annual growth rate of more than 15%, while the growth rate slows down after 2029, with an average annual growth rate of 4.44%.In 2033, the number of leisure tourism tourists will reach 1,691,280,000, and the leisure tourism preference of tourism users has been significantly strengthened.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".