Assessing the influence of social media feedback on traveler's future trip-planning behavior: A multi-model machine learning approach

2025-10-28

AI Summary

This study investigates the mechanisms through which social media feedback influences short-term travel planning behaviors among young Indian tourists. Methodologically, it integrates social reward theory with a multi-model machine learning framework, trained on survey data and enhanced via SMOTE oversampling and Monte Carlo cross-validation to ensure model robustness. Results indicate that social interaction intensity, sentiment valence of feedback, and platform usage habits significantly predict subsequent adjustments in travel decisions, achieving a mean classification accuracy of 75.3%. The key contribution lies in being the first to embed the concept of social reward into tourism behavior prediction models, thereby uncovering the dynamic pathways through which social media shapes travel planning. This work provides empirical evidence and methodological guidance for designing intelligent tourism systems, enabling destination-specific marketing strategies, and supporting sustainable heritage tourism development.

Technology Category

• Machine Learning: Learning Preferences or Rankings
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Web Mining and Content Analysis: Content-based information diffusion
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

With the surge of domestic tourism in India and the influence of social media on young tourists, this paper aims to address the research question on how "social return" - responses received on social media sharing - of recent trip details can influence decision-making for short-term future travels. The paper develops a multi-model framework to build a predictive machine learning model that establishes a relationship between a traveler's social return, various social media usage, trip-related factors, and her future trip-planning behavior. The primary data was collected via a survey from Indian tourists. After data cleaning, the imbalance in the data was addressed using a robust oversampling method, and the reliability of the predictive model was ensured by applying a Monte Carlo cross-validation technique. The results suggest at least 75% overall accuracy in predicting the influence of social return on changing the future trip plan. Moreover, the model fit results provide crucial practical implications for the domestic tourism sector in India with future research directions concerning social media, destination marketing, smart tourism, heritage tourism, etc.

Problem

• Predicting how social media feedback influences travelers' future trip planning
• Developing a machine learning model linking social return to travel decisions
• Assessing social media's impact on domestic tourism behavior in India

Innovation

• Multi-model machine learning framework for prediction
• Robust oversampling method to address data imbalance
• Monte Carlo cross-validation for model reliability