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.

翻译：随着印度国内旅游业的激增以及社交媒体对年轻游客的影响，本文旨在探讨一个研究问题：近期旅行细节在社交媒体分享中获得的‘社交回报’——即收到的回应——如何影响短期未来旅行的决策。本文构建了一个多模型框架，以建立预测性机器学习模型，该模型关联了旅行者的社交回报、多种社交媒体使用行为、旅行相关因素及其未来行程规划行为。主要数据通过针对印度游客的问卷调查收集。经过数据清洗后，采用稳健的过采样方法处理数据不平衡问题，并通过蒙特卡洛交叉验证技术确保预测模型的可靠性。结果表明，该模型在预测社交回报对改变未来旅行计划的影响方面总体准确率至少达到75%。此外，模型拟合结果为印度国内旅游业提供了重要的实践启示，并指出了未来在社交媒体、目的地营销、智慧旅游、遗产旅游等领域的研究方向。