Uso de métricas de interactividad para la interpretación de publicaciones de una fan page institucional en Facebook
Nowadays digital landscape, enhancing user engagement on social media platforms is crucial for elevating the visibility of universities within their respective communities. Through online presence, universities can transform their research activities and outcomes into valuable assets that extend wel...
| Autores: | , , , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2023 |
| País: | México |
| Institución: | UNIVERSIDAD DE GUADALAJARA |
| Repositorio: | ReCIBE. Revista Electrónica de Computación, Informática, Biomédica y Electrónica |
| Idioma: | inglés |
| OAI Identifier: | oai:ojs.recibe.cucei.udg.mx:article/305 |
| Acceso en línea: | http://recibe.cucei.udg.mx/index.php/ReCIBE/article/view/305 |
| Access Level: | acceso abierto |
| Palabra clave: | Social media Interactivity User Engagement Facebook fan page Spearman's Correlation Coefficient Clustering |
| Sumario: | Nowadays digital landscape, enhancing user engagement on social media platforms is crucial for elevating the visibility of universities within their respective communities. Through online presence, universities can transform their research activities and outcomes into valuable assets that extend well beyond the academia. As a result, the relationship with their community strengthens, opening new opportunities for collaboration. Interaction data from Social networks allows to identify the elements of User Engagement and, with this data, it is possible to comprehend user behavior in order to create an online university community. A quantitative analysis of user engagement, viewed through university's commitment to its community, was conducted. The research findings offer a strategic tool to enhance the dissemination of scientific content geared toward user engagement. This study was carried out using data from a university's Facebook fan page, which served as a platform for sharing scientific content, news, and event updates. The research followed a methodology for analyzing social network data, which enabled the identification of key elements contributing to user engagement. The investigation revealed that variations in engagement for individual posts could be explained by a regression model, using the most correlated variables extracted from the fan page's interaction report. Further exploration, employing data mining techniques, underscored that self-generated content played the most pivotal role in driving user engagement. |
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