Introducción y objetivos: Las bases de datos académicas permiten llevar a cabo revisiones bibliográficas que estén libres de sesgos y que a la vez tengan la máxima transparencia y trazabilidad.Método: Presentamos, mediante preguntas y respuestas, las claves principales que conectan el uso de bases de datos académicas con las revisiones bibliográficas, sistemáticas o tradicionales.Resultados: En cada apartado se presentan enlaces a entradas relacionadas donde se amplían los temas tratados para quienes deseen profundizar en ellos.Al final se presenta una bibliografía recomendada y se destacan las dos importantes obras en las que hemos basado principalmente este y otros trabajos anteriores sobre el mismo tema.Conclusiones: Hemos presentado, en un sistema de preguntas y respuestas, los elementos esenciales de la relación entre bases de datos académicas y revisiones bibliográficas. Para entender esta relación hemos presentado previamente las funciones de las revisiones bibliográficas y sus productos principales. También hemos discutido con cierto detalle las características de la principal dicotomía que existe en las revisiones bibliográficas, a saber, las tradicionales y las sistemáticas, con su alternativa para Ciencias Sociales y Humanidades, que no siempre trabajan sobre intervenciones o sobre resultados de investigación, que son las aproximaciones sistemáticas.En el apartado de las bases de datos académicas hemos desarrollado un grupo de preguntas y respuestas sobre su utilización. Mostrando los componentes principales de las bases de datos académicas, así como un procedimiento basado en 6 fases, han sido mostrados igualmente.En la mayoría de los apartados, además hemos añadido enlaces a recursos relacionados, consistentes en entradas publicadas en este mismo sitio donde se desarrollan, in extenso, algunos de tales apartados.

Artificial intelligence (AI) is already integrated into news production strategies in some media outlets. Recently, generative AIs such as ChatGPT and others have demonstrated their ability to enhance productivity in content production tasks, raising the question of how journalism faculties can address this new technology. This paper presents an academic study on the application of AI in higher communication studies. The study involved 4 in-depth interviews and 28 semi-structured interviews with university lecturers and researchers. The findings confirm varying degrees of convergence and divergence on different aspects of the technology, including the integration of AI in communication faculties, student training in AI usage, the introduction of AI and journalism as a subject area, and the potential uses of AI in news production and consumption. Additionally, this paper proposes a comprehensive training program on AI and journalism, focusing on its foundations, technical competencies and ethical considerations.

The visibility of academic articles or conference papers depends on their being easily found in academic search engines, above all in Google Scholar. To enhance this visibility, search engine optimization (SEO) has been applied in recent years to academic search engines in order to optimize documents and, thereby, ensure they are better ranked in search pages (i.e., academic search engine optimization or ASEO). To achieve this degree of optimization, we first need to further our understanding of Google Scholar’s relevance ranking algorithm, so that, based on this knowledge, we can highlight or improve those characteristics that academic documents already present and which are taken into account by the algorithm. This study seeks to advance our knowledge in this line of research by determining whether the language in which a document is published is a positioning factor in the Google Scholar relevance ranking algorithm. Here, we employ a reverse engineering research methodology based on a statistical analysis that uses Spearman’s correlation coefficient. The results obtained point to a bias in multilingual searches conducted in Google Scholar with documents published in languages other than in English being systematically relegated to positions that make them virtually invisible. This finding has important repercussions, both for conducting searches and for optimizing positioning in Google Scholar, being especially critical for articles on subjects that are expressed in the same way in English and other languages, the case, for example, of trademarks, chemical compounds, industrial products, acronyms, drugs, diseases, etc.

Search engine optimization (SEO) constitutes the set of methods designed to increase the visibility of, and the number of visits to, a web page by means of its ranking on the search engine results pages. Recently, SEO has also been applied to academic databases and search engines, in a trend that is in constant growth. This new approach, known as academic SEO (ASEO), has generated a field of study with considerable future growth potential due to the impact of open science. The study reported here forms part of this new field of analysis. The ranking of results is a key aspect in any information system since it determines the way in which these results are presented to the user. The aim of this study is to analyze and compare the relevance ranking algorithms employed by various academic platforms to identify the importance of citations received in their algorithms. Specifically, we analyze two search engines and two bibliographic databases: Google Scholar and Microsoft Academic, on the one hand, and Web of Science and Scopus, on the other. A reverse engineering methodology is employed based on the statistical analysis of Spearman’s correlation coefficients. The results indicate that the ranking algorithms used by Google Scholar and Microsoft are the two that are most heavily influenced by citations received. Indeed, citation counts are clearly the main SEO factor in these academic search engines. An unexpected finding is that, at certain points in time, Web of Science (WoS) used citations received as a key ranking factor, despite the fact that WoS support documents claim this factor does not intervene.

La creciente adaptación de los medios de comunicación al entorno digital hace conveniente la utilización de metodologías de análisis que permitan determinar la calidad de sus sitios web. En este artículo, a partir del estudio de las características de las webs de algunos de los medios de comunicación más importantes a nivel internacional, se propone una metodología para la evaluación específica de este tipo de sitios web. El modelo propuesto se compone de treinta y seis indicadores, organizados en torno a ocho parámetros que permiten valorar, especialmente, la adaptación del medio de comunicación a la interacción de la Web 2.0. Otros aspectos a los que también se presta atención, pero en menor medida, son la arquitectura de la información, la usabilidad, la accesibilidad o las herramientas de interacción y comunicación que éste pone a disposición de sus usuarios.

Periodismo computacional: evolución, casos y herramientas