Why is a new list of tourist destinations needed?

South Korea's tourism industry has grown over the past several decades, but its yardstick has remained visitor numbers. In an era of hyper-personalization, where individual preferences are becoming increasingly diverse, such quantitative metrics alone cannot reveal whether visitors were actually satisfied or whether they would genuinely recommend a place.

Existing tourism ranking materials tend to be subjective, relying heavily on recommendation committees, or skewed toward commercial criteria — resulting in the repeated selection of already-famous destinations and a failure to cover the country broadly. In addition, because digital platforms (where tourism promotion mainly takes place) tend to make "popular places even more popular" (the Matthew effect), regional imbalance and delays in discovering hidden destinations have intensified.

In response, Yanolja Research's Korea's Top 500 Tourist Destinations surveys nearly the entire set of tourist destinations across 229 local governments nationwide, and adds social-data-based sentiment analysis to quantitatively measure both awareness and emotional response toward each destination — aiming to help resolve the tourism sector's persistent challenges of supply-demand imbalance and regional concentration.

Theoretical Background

Today's travelers are no longer passive observers. They have become active agents who search for information in real time on digital platforms, design their own itineraries to match personal tastes, and spread their experiences further through their networks. Korea's Top 500 Tourist Destinations reflects this structural shift in traveler behavior by applying an integrated analytical framework that reinterprets, for the digital environment, the Stimuli-Organism-Response (S-O-R) theory from environmental psychology and the Information Adoption Model (IAM) from information systems research, while also accounting for the Matthew Effect.

Tourism behavior can be explained through social data because traveler behavior itself follows a stimulus-response structure. According to S-O-R theory, external stimuli affect an individual's internal state (organism), which in turn triggers a behavioral response. Digital content such as Instagram short-form videos, YouTube vlogs, and blog posts acts as a stimulus that directly engages travelers' senses and emotions, driving the behavior of an actual visit. In other words, the mentions and reactions left in social data are not mere opinion — they are traces recording both the stimulus that triggered real tourism behavior and its outcome.

This raises the question of which criteria determine whether a piece of content is actually adopted by travelers and translated into action — a question the IAM model addresses. The IAM model can be likened to "waiting in line at a popular restaurant." Buzz volume (mention volume) is like the line outside a restaurant: even without having visited yet, it provides social proof — "since so many people have gone there, it must be worth it" — which lowers the uncertainty of choice. But just as a long line doesn't guarantee the food is excellent, a high mention volume doesn't guarantee that a destination is genuinely satisfying. Only when sentiment — how positively those who actually experienced the destination felt — backs up the buzz does the real appeal that leads to revisits and recommendations become confirmed. If mention volume is the signal that lowers the threshold for initial engagement, sentiment is the signal that shows whether the experience was actually good.

The need to consider both indicators together is also evident in the structural bias of digital platforms. Recommendation algorithms expose destinations that already have high mention volume more frequently, and that exposure in turn increases mention volume further, creating a virtuous cycle. Meanwhile, destinations that are genuinely highly satisfying but have low initial mention volume can be buried without ever being picked up by the algorithm — the Matthew Effect. Korea's Top 500 Tourist Destinations surveys nearly all tourist destinations across 229 local governments nationwide to prevent such destinations from being overlooked from the outset, and gives mention volume and sentiment equal weight as ranking criteria so that destinations with high sentiment but lower mention volume can still receive a fair ranking.

Accordingly, Korea's Top 500 Tourist Destinations measures each destination's appeal along two core axes: mention volume and sentiment.

Methodology

To evaluate tourist destinations based on social big data, Yanolja Research selects the local governments, destinations, and channels to be analyzed, collects data, and then applies a developed evaluation index to select the final 500 destinations.

1Purpose

• To provide rankings of tourist destinations by measuring both their awareness and their appeal through sentiment analysis of social data
• To discover hidden domestic destinations by analyzing not only awareness but also visitors' sentiment

2Evaluation Framework

This model draws on Stimuli-Organism-Response (S-O-R) theory and the Information Adoption Model (IAM) as its theoretical basis, and structures a destination's appeal along two axes.

3Data Collection

4Measurement Method

To prevent distortion that would result from simply combining mention volume and sentiment scores — which operate on different scales — Korea's Top 500 Tourist Destinations uses an evaluation model based on relative rankings rather than absolute values.

Step 1. Calculating Mention Volume and Sentiment Scores

Based on social data and sentiment analysis results for each destination, (1) buzz volume and (2) sentiment scores are calculated.

Step 2 & 3. Individual Rankings, Integrated Ranking & Tie Handling

Based on mention volume and sentiment scores, separate mention-volume rankings and sentiment rankings are calculated for each destination. The average of the sentiment ranking and the mention-volume ranking is then calculated to determine the final integrated ranking.
※ When integrated rankings are tied, priority goes to the destination with the higher mention volume.

Selected destinations are graded by rank into Tier 1 (ranks 1–100), Tier 2 (ranks 101–300), and Tier 3 (ranks 301–500, detailed rankings undisclosed). In addition, the country is divided into 9 regions — including Seoul, Busan, Jeju, and others — with regional rankings published separately, along with rankings by destination type (Natural Landscape / History & Culture / Entertainment).