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

TL;DR

This study uses a multi-model machine learning framework to analyze how social media feedback influences Indian tourists' future trip planning, achieving over 75% prediction accuracy.

Contribution

It introduces a novel multi-model machine learning approach to predict travel behavior based on social media responses, addressing data imbalance and validating model reliability.

Findings

At least 75% accuracy in predicting social influence on trip plans

Effective handling of data imbalance with oversampling techniques

Provides practical insights for domestic tourism marketing strategies

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.