Artificial intelligence and journalism: Systematic review of scientific production in Web of Science and Scopus (2008-2019)

Research about the use of Artificial Intelligence applied to journalism has increased over the years. The studies conducted in this field between January 2008 and December 2019 were analysed to understand the contexts in which they have been developed and the challenges detected. The method used consisted of a systematic review of the scientific literature (SLR) of 209 scientific documents published in the Web of Science and Scopus databases. The validation required the inclusion and exclusion criteria, database identification, search engines and evaluation and description of results. The findings indicate that the largest number of publications related to this topic are concentrated in the United States and that the rise of scientific production on Artificial Intelligence in journalism takes place in 2015, when the remarkable growth of these publications begins, until reaching 61 in 2019. It is concluded that research is mainly published in scientific journals, which include works that handle a broad variety of topics, such as information production, data journalism, big data, application in social networks or information checking. In relation to authorship, the trend is the presence of a single signer.La investigación sobre el uso de la Inteligencia Artificial aplicada al periodismo se ha intensificado en los últimos años. Este artículo analiza los estudios producidos en este campo entre enero 2008 y diciembre 2019, a fin de conocer qué investigaciones se han realizado y cuáles son los contextos en los que se han desarrollado. El método ha sido una revisión sistemática de la literatura científica (SLR) de 209 documentos científicos publicados en las bases de datos Web of Science y Scopus. La validación ha seguido los criterios de inclusión y exclusión, identificación de la base de datos, motores de búsqueda y evaluación y descripción de resultados. Los hallazgos indican que en Estados Unidos se concentra el mayor número de publicaciones relacionadas con este tema y que el auge de la producción científica sobre la Inteligencia Artificial en periodismo se produce en 2015, cuando empieza el crecimiento notable de estas publicaciones, hasta alcanzar las 61 en 2019. Se concluye que las investigaciones se publican principalmente en revistas científicas, que incluyen trabajos que versan sobre una amplia variedad de temas, como la producción informativa, el periodismo de datos, el big data, la aplicación en redes sociales o el chequeo de información. En relación con la autoría, la tendencia es la presencia de un único firmante

Journalists' Perceptions towards Employing Artificial Intelligence Techniques in Jordan TV's Newsrooms

The study aimed to reveal the perceptions of journalists towards employing artificial intelligence techniques in Jordan TV's Newsrooms. The study sample consisted of journalists working in Jordan TV's Newsrooms. The field study was applied to a simple random sample of (106) through the questionnaire tool. The study used the descriptive exploratory approach. The study concluded that there is a clear effect of employing artificial intelligence techniques according to the perceptions of journalists in the newsrooms of Jordan Television, and the presence of differences between the responses of the respondents of the journalists in the reasons why the newsrooms of the Jordanian television are not ready to employ artificial intelligence techniques. In addition to the above, the study indicated that there were no statistically significant differences in the other reasons for the lack of readiness, where the values of X2 (Chi-squared) were not a significant. However, journalists in the newsrooms of Jordan Television possessed various skills, including: using social networks for research, publishing news stories, and automated content production programs. Finally, there were no statistically significant differences between journalists in digital media skills, and the X2 values were not significant at the significance level (0.05), with the exception of info graphic production skills

Transparency Policies in European Public Broadcasters: Sustainability, Digitalisation and Fact-Checking

Over the last few years, European public broadcasters have promoted the concept of public service media as one of their main values. To this end, transparency policies have been implemented as a mechanism of corporate projection by strengthening their role as an essential service. The objective of this article is to ascertain the existence of this type of policies among European public broadcasters. To this end, a nominal group was made with 24 experts who were surveyed, thus generating new indicators of transparency and accountability strategies around sustainability and digitalization. The contents of the websites of RTVE (Spain), RTP (Portugal), France TV (France), RAI (Italy), BBC (UK), RTÉ (Ireland), ZDF (Germany), VRT (Belgium), and SVT (Sweden) were also analyzed, paying attention to such indicators and strategies. The main results include the identification of differences on the basis of the ideal models described by Hallin and Mancini; a commitment to credibility (fact-checking) to the detriment of diversity of opinions; and a connection between the political system and the media system, which, preliminarily, determines the level of transparency of these public entitiesThe results of this research are part of the work of the “Equipo de Investigaciones Políticas” (USC). It is also part of the project “Nuevos valores, gobernanza, financiación y servicios audiovisuales públicos para la sociedad de Internet: contrastes europeos y españoles” (2019–2021) financed by the Spanish Ministry of Innovation and Universities (RTI2018-096065-B-I00)S

Mapping AI Arguments in Journalism Studies

This study investigates and suggests typologies for examining Artificial Intelligence (AI) within the domains of journalism and mass communication research. We aim to elucidate the seven distinct subfields of AI, which encompass machine learning, natural language processing (NLP), speech recognition, expert systems, planning, scheduling, optimization, robotics, and computer vision, through the provision of concrete examples and practical applications. The primary objective is to devise a structured framework that can help AI researchers in the field of journalism. By comprehending the operational principles of each subfield, scholars can enhance their ability to focus on a specific facet when analyzing a particular research topic

Data Science, Machine learning and big data in Digital Journalism: A survey of state-of-the-art, challenges and opportunities

Digital journalism has faced a dramatic change and media companies are challenged to use data science algo-rithms to be more competitive in a Big Data era. While this is a relatively new area of study in the media landscape, the use of machine learning and artificial intelligence has increased substantially over the last few years. In particular, the adoption of data science models for personalization and recommendation has attracted the attention of several media publishers. Following this trend, this paper presents a research literature analysis on the role of Data Science (DS) in Digital Journalism (DJ). Specifically, the aim is to present a critical literature review, synthetizing the main application areas of DS in DJ, highlighting research gaps, challenges, and op-portunities for future studies. Through a systematic literature review integrating bibliometric search, text min-ing, and qualitative discussion, the relevant literature was identified and extensively analyzed. The review reveals an increasing use of DS methods in DJ, with almost 47% of the research being published in the last three years. An hierarchical clustering highlighted six main research domains focused on text mining, event extraction, online comment analysis, recommendation systems, automated journalism, and exploratory data analysis along with some machine learning approaches. Future research directions comprise developing models to improve personalization and engagement features, exploring recommendation algorithms, testing new automated jour-nalism solutions, and improving paywall mechanisms.Acknowledgements This work was supported by the FCT-Funda?a ? o para a Ciência e Tecnologia, under the Projects: UIDB/04466/2020, UIDP/04466/2020, and UIDB/00319/2020

Innovative innovation in journalism

Previous studies over the past 23 years have illustrated how the journalistic field has embraced innovative processes. While some of these processes were fully developed at that time, others were still in the process of development or implementation. An ad hoc analysis sheet was designed using innovation categories, where each item is assigned a score based on the level of innovation, ranging from low to high. This methodological instrument is proposed for the analysis of high innovation in news websites and it is applied to narratives, data journalism, audience involvement, co-creation, verification, ethics, corporate information and content distribution in the most widely consumed news sites across Europe, the Americas, Asia Pacific and Africa. What is discovered is that there are no more evolved trends in some regions than in others. Nevertheless, European and American sites offer a broader range of options compared to their African and Asian counterparts.1. Ministry of Science, Innovation and Universities (Spain), and co-financed by the European Regional Development Fund (ERDF) [PID2021-122534OB-C21. Digital native media in Spain: Strategies, competencies, social involvement and (re)definition of practices in journalistic production and dissemination]. 2. Project IBERIFIER - Iberian Digital media Research and Fact-Checking Hub, action number 2020-EU-IA-0252. Co-financed by the Connecting Europe Facility of the European Union (CEF-TC-2020-2).S

Use of generative artificial intelligence in the training of journalists: challenges, uses and training proposal

Artificial Intelligence (AI) is already integrated into news production strategies in some media outlets. Recently, generative AIs like 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 Relevance of Technology to Information Verification: Insights from Norwegian Journalism During a National Election

Growing concerns about disinformation have led to the development of new digital tools and systems designed for journalists’ verification and fact-checking needs. Despite these technological developments, research has demonstrated that emerging technologies are not utilised as often and are not as highly valued as industry narratives suggest. There are indications that the typical journalist values traditional skills such as writing and interviewing higher than digital technology skills and that many journalists do not consider the new tools to be very useful in their everyday work. This article takes on a sociotechnical approach to study the interplay between journalists, technology, organisational and professional routines. Specifically, we examine journalists’ use of verification technologies to detect disinformation during an election period. Our findings show a discrepancy between the alleged potential of new technologies and the everyday practices of newswork and fact-checking – also in the digitally advanced Norwegian media industry. We found tensions between established routines and cultures in the newsroom and the push for the renewal of journalistic methods which can be sorted under two headings: strategy vs. practice and proximity vs. distance to the beat and sources.The Relevance of Technology to Information Verification: Insights from Norwegian Journalism During a National ElectionpublishedVersio

Giving Computers a Nose for News: Exploring the limits of story detection and verification

The use of social media as a source of news is entering a new phase as computer algorithms are developed and deployed to detect, rank, and verify news. The efficacy and ethics of such technology is the subject of this article, which examines the SocialSensor application, a tool developed by a multidisciplinary European Union research project. The results suggest that computer software can be used successfully to identify trending news stories, allow journalists to search within a social media corpus, and help verify social media contributors and content. However, such software also raises questions about accountability as social media is algorithmically filtered for use by journalists and others. Our analysis of the inputs SocialSensor relies on shows biases towards those who are vocal and have an audience, many of whom are men in the media. We also reveal some of the technology’s temporal and topic preferences. The conclusion discusses whether such biases are necessary for systems like SocialSensor to be effective. The article also suggests that academic research has failed to recognise fully the changes to journalists’ sourcing practices brought about by social media, particularly Twitter, and provides some countervailing evidence and an explanation for this failure