Algorithmic Science News: support platform for science journalism

• Autor(a) principal: Silva, Joana
• Data de Publicação: 2019
• Outros Autores: Fernandes, Maria, Pasquali, Arian, Loureiro, António, Silva, Filipa, Candeias, Pedro, Lima, Helena, Reis, Ana Isabel, Giesteira, Bruno, Jorge, Alípio, Nunes, Sérgio
• Tipo de documento: Livro
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: https://hdl.handle.net/10216/151375
• topic: Ciências da comunicação; Communication sciences

The Algorithmic Science News (ASN) platform is a new tool created by a multidisciplinary team that emerged from the need to reinscribe the role of science news in today's media space. The platform aims not only to increase the number of scientific news available to publishers, but also to reduce the effort associated with more time-consuming tasks such as collecting data and analyzing scientific articles and thereby facilitating the entire news production process. The ASN platform includes a set of features designed to support the journalist's standard procedure in writing contexts, allowing the use of documents in open access scientific repositories. The development of algorithms working on these repositories followed the purpose of facilitating the access and exploration of these collections and allowing the media to use them as an information source. In this project we developed tools of reading and interpretation, writing and semantic suggestion. With regard to reading and interpretation, ASN helps on finding specialists related to the scientific article, summarizes the most determinant parts, namely the introduction, objectives, methodologies and results, present definitions of technical terms, and suggest projects related to the topic. With regard to the writing part, the platform allows to write notes related to parts of the article and have access to phrases suggestions. The use of a beta version of this platform in writing contexts will allow the realization of the extent to which the automation of tasks associated with journalistic production can help the media in transition to digital, as well as to contribute to a greater efficiency in the tasks associated with science journalism, allowing a greater massification and quality in the production of scientific news in the current media landscape.