A Review of Multimodal and Retrieval-Augmented Artificial Intelligence Approaches for Social Media-Based Travel Discovery

Abstract

In recent years, social media platforms have evolved into primary sources of travel inspiration, with travelers increasingly relying on short-form video content to discover unique, ”off-the-beaten-path” locations. Despite their popularity, these videos often lack the structured and reliable information necessary for practical travel planning. Critical details such as precise locations, accessibility, and logistical guidance are frequently fragmented across captions and comments or omitted entirely, creating a significant gap between visual discovery and actionable decision-making. This paper reviews existing research in social media mining, multimodal information extraction, and Natural Language Processing (NLP) to determine how Artificial Intelligence can bridge this gap. Building on insights from the reviewed literature, the study presents the conceptual design of ”ReelScout,” an AI-driven platform that integrates Computer Vision and NLP to analyze social media reels for identifying hidden Points of Interest (POIs). By synthesizing multimodal cues from visual content, audio narration, and textual metadata, the proposed framework aims to organize unstructured social media data into meaningful travel knowledge. Finally, this review highlights current methodological trends, limitations, and future research directions for the development of intelligent, social-media-driven travel discovery systems.

Introduction

The text reviews how the rapid growth of short-form social media videos (reels and shorts) has transformed travel inspiration and location discovery, while also highlighting the limitations of such content for practical travel planning. Although visually engaging, social media posts often lack structured and reliable information such as exact locations, accessibility, safety, and logistics, making it difficult for users to convert inspiration into actionable decisions.

The large volume of unstructured, multimodal data (text, images, audio, and video) generated on social media poses significant challenges for traditional data analysis methods. Recent advances in Artificial Intelligence, including Natural Language Processing (NLP), Computer Vision (CV), multimodal learning, and large language models, have enabled more effective extraction of insights from social media content. In particular, multimodal approaches that combine text, visuals, and audio outperform unimodal methods, while Retrieval-Augmented Generation (RAG) improves factual accuracy by grounding AI outputs in external knowledge sources—an important requirement for travel recommendation and decision-support systems.

The literature review traces the evolution of research from early text- and metadata-based POI discovery to vision-based, multimodal, and RAG-based systems. Text-based approaches are scalable but limited by noisy and informal language, while vision-based methods excel at recognizing well-known landmarks but struggle with ambiguous or lesser-known locations. Multimodal systems provide richer contextual understanding but introduce higher computational complexity. RAG-based systems further enhance reliability and adaptability by incorporating up-to-date external information.

The paper proposes a taxonomy of existing approaches—text-based, vision-based, multimodal, and RAG-based—and presents a comparative analysis highlighting trade-offs among accuracy, scalability, and system complexity. Despite methodological advances, key challenges remain, including noisy and incomplete data, multimodal fusion complexity, high computational cost, lack of standardized evaluation benchmarks, and ethical and privacy concerns related to implicit geolocation inference.

Finally, the review identifies future research directions, emphasizing the need for advanced multimodal fusion strategies, explainable and interpretable AI systems, privacy-preserving methods, and robust evaluation frameworks. Overall, the text concludes that while AI-driven multimodal and retrieval-grounded approaches show strong potential for social media-based travel discovery and recommendation, further research is required to ensure scalability, reliability, and ethical deployment in real-world applications.

Conclusion

This review presented a comprehensive and structured analysis of artificial intelligence-based approaches for social media- driven travel discovery and recommendation systems. By systematically categorizing existing literature into text-based, vision- based, multimodal, and retrieval-augmented approaches, the paper highlighted the evolution of methodologies and the growing reliance on content-aware and data-driven techniques. The comparative analysis further revealed the strengths and limitations of representative studies, illustrating a clear shift from metadata-driven methods toward multimodal and retrieval-grounded frameworks capable of richer contextual understanding. The discussion of challenges emphasized critical issues related to data noise, multimodal fusion complexity, scalability, evaluation, and ethical considerations, underscoring the limitations of current solutions in real-world deployment scenarios. By identifying these open research problems and outlining future research directions, this review provides a structured foundation for advancing intelligent, reliable, and responsible travel discovery systems. Overall, the insights presented in this paper aim to support researchers and practitioners in designing next-generation AI solutions that effectively bridge the gap between social media-based visual inspiration and actionable travel planning. As social media platforms continue to evolve, the synthesis offered by this review is intended to guide future work at the intersection of social media analytics, artificial intelligence, and intelligent tourism systems.

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Copyright

Copyright © 2026 Aaron Anil Zachariah, Amith Krishna K, Athira Santhosh, Bharath M Nanda, Mrithu A S. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.