Hybridizing metric learning and case-based reasoning for adaptable clickbait detection.
[EN]The term clickbait is usually used to name web contents which are specifically designed to maximize advertisement monetization, often at the expense of quality and exactitude. The rapid proliferation of this type of content has motivated researchers to develop automatic detection methods, to eff...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2018 |
| País: | España |
| Institución: | Universidad de Salamanca (USAL) |
| Repositorio: | GREDOS. Repositorio Institucional de la Universidad de Salamanca |
| OAI Identifier: | oai:gredos.usal.es:10366/157257 |
| Acceso en línea: | http://hdl.handle.net/10366/157257 |
| Access Level: | acceso abierto |
| Palabra clave: | Clickbait detection Metric learning Case-based reasoning Neural networks 1203.04 Inteligencia Artificial 1203.17 Informática |
| Sumario: | [EN]The term clickbait is usually used to name web contents which are specifically designed to maximize advertisement monetization, often at the expense of quality and exactitude. The rapid proliferation of this type of content has motivated researchers to develop automatic detection methods, to effectively block clickbaits in different application domains. In this paper, we introduce a novel clickbait detection method. Our approach leverages state-of-the-art techniques from the fields of deep learning and metric learning, integrating them into the Case-Based Reasoning methodology. This provides the model with the ability to learn-over-time, adapting to different users’ criteria. Our experimental results also evidence that the proposed approach outperforms previous clickbait detection methods by a large margin. |
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