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

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.