Social Media, Big Data Analytics, and NLP Techniques for Decision-Making Purposes in Sustainable Tourist Behavior

Leonidas Theodorakopoulos, Alexandra Theodoropoulou and Konstantinos Giotopoulos ()
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Leonidas Theodorakopoulos: University of Patras, Department of Management Science and Technology
Alexandra Theodoropoulou: University of Patras, Department of Management Science and Technology
Konstantinos Giotopoulos: University of Patras, Department of Management Science and Technology

A chapter in Synergizing Management, Culture, and Arts for Tourism Development - Vol. 2, 2026, pp 135-175 from Springer

Abstract: Abstract The digital transformation of tourism has given rise to vast streams of user-generated data, offering new avenues for understanding and influencing sustainable tourist behavior. This comprehensive review investigates the synergistic role of social media, big data analytics, and natural language processing (NLP) in enhancing data-driven decision-making for sustainable tourism. By synthesizing recent interdisciplinary literature, this chapter explores how platforms such as Instagram and TripAdvisor generate behavioral signals and how NLP techniques—such as sentiment analysis, topic modeling, and emotion detection—can extract meaningful insights from unstructured content. These insights, when integrated into big data systems, inform real-time decision-making, predictive modeling, and personalized communication strategies aimed at promoting sustainability. The review also identifies emerging trends, ethical considerations, and key challenges in applying NLP across diverse linguistic and cultural tourism contexts. Ultimately, it highlights the transformative potential of data-driven intelligence for building adaptive, responsible, and future-ready tourism ecosystems.

Keywords: Sustainable tourism; Social media analytics; Personalized marketing; Behavioral insights; Digital transformation in tourism (search for similar items in EconPapers)
JEL-codes: L86 M31 O32 O33 Z32 Z33 (search for similar items in EconPapers)
Date: 2026
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DOI: 10.1007/978-3-032-17534-2_4

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