AI-Based Webtoon Asset Valuation Model: Utilization and Potential of LLM

Abstract

The rapid growth of the digital content industry and the global expansion of webtoons have highlighted the need for a systematic approach to evaluating the economic value of webtoons. However, traditional asset valuation methods, primarily designed for physical assets, face limitations in adequately reflecting the economic value of intangible assets such as digital content. To address this issue, this study proposes an AI-based webtoon asset valuation model using data from Naver Webtoon. Following the theoretical framework for intangible asset evaluation, key metrics such as view counts, ratings, and user engagement were identified based on the income approach, cost approach, and market approach. The economic value of individual webtoons was assessed through a Single Grading methodology. Additionally, qualitative data was quantified using Large Language Models (LLMs) to enhance the reliability and validity of the evaluations. This study demonstrates that AI-based asset valuation models can overcome the limitations of traditional methods and suggests their potential as a standardized evaluation system within the digital content industry.