Detecting AI adoption at scale: a web mining and LLM methodology
[EN] The recent emergence of Artificial Intelligence (AI) technologies has created multiple opportunities for companies. The growing interest in AI adoption has led to an increase in technological innovations and business applications. At the same time, web mining techniques have been used to monito...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | inglés |
| OAI Identifier: | oai:dnet:riunet______::716115516a9f109b43a23bcb805ff80c |
| Acceso en línea: | https://riunet.upv.es/handle/10251/233398 |
| Access Level: | acceso abierto |
| Palabra clave: | Artificial intelligence Large language models Web scraping Technology mining AI adoption |
| Sumario: | [EN] The recent emergence of Artificial Intelligence (AI) technologies has created multiple opportunities for companies. The growing interest in AI adoption has led to an increase in technological innovations and business applications. At the same time, web mining techniques have been used to monitor technological trends, often through basic natural language processing methods. However, advances in Large Language Models (LLMs) enable a more detailed and in-depth analysis of web content, necessitating the development of new methodologies. This paper proposes a methodology for integrating LLMs into the technology mining of corporate websites, with a focus on Spanish companies. The study uses data collected in 2023 and 2025, applying web scraping techniques to gather textual information. The analysis process involves automated AI detection, manual validation, and evaluation through standard classification metrics. The findings contribute to a better understanding of AI adoption trends and inform future research on tech mining applications for industry. |
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