Mapping the field of Algorithmic Journalism

Abstract

With software automatically producing texts in natural language from structured data, the evolution of natural language generation (NLG) is changing traditional news production. The paper first addresses the question whether NLG is able to perform the functions of professional journalism on a technical level. A technological potential analysis therefore uncovers the technological limitations and possibilities of NLG, accompanied by an institutional classification following Weischenberg, Malik, and Scholl. Overall, NLG is explained within the framework of algorithmic selection and along its technological functionality. The second part of the paper focuses on the economic potential of NLG in journalism as well as indicating its institutionalization on an organizational level. Thirteen semi-structured interviews with representatives of the most relevant service providers detail the current market situation. Following Heuss, the development of the NLG market is classified into phases. In summary, although the market for NLG in journalism is still at an early stage of market expansion, with only a few providers and journalistic products available, NLG is able to perform tasks of professional journalism at a technical level. The analysis therefore sets the basis to analyze upcoming challenges for journalism research at the intersection of technology and big data.

Automation, bots and algorithms in newsmaking. Impact and quality of artificial journalism

“…It is also referred as algorithm journalism (Diakopoulos, 2015;Dörr, 2016) and robot journalism (Oremus, 2015), but it is always identified as a technological solution to produce news and to carry out journalistic tasks such as reports, curation and even data analysis and visualization (Gao et al, 2014;Shearer, Basile and Geiger, 2014;Broussard 2015;Carlson 2015;Young and Hermida 2015).…”

“…From the results of the detailed analysis of databases and scientific social networks, it can be noted that research focused on the impact of technological advances in journalism (Powers 2012;Karlsen et al, 2014) and the use of computers as tools to increase interactivity with users (Flew et al, 2012), pave the way for works on the application of AI in newsmaking, such as contributions by Kim et al, 2007;Matsumoto et al, 2007;Napoli, 2012;Van Dalen, 2012;Clerwall, 2014;Edge, 2014;Karlsen and Stavelin, 2014;Latar, 2014;Stavelin, 2014;Carlson, 2015;Oremus, 2015;Lecompte (2015); Dörr (2016); Graefe (2016) Research from Graefe (2016) and Dörr (2016) stand out, where they identify media outlets using computer-generated news, and the Fanta report (2017) on the use of computer-generated news in European press agencies. They affirm that automated journalism may say what is happening and when, but not why.…”

Comunicación algorítmica en los partidos políticos: automatización de producción y circulación de mensajes

“…Términos como "periodismo robot", "periodismo automatizado", "periodismo algorítmico" o "periodismo escrito a máquina" dominan los medios y el discurso científico (Anderson, 2013;Dörr, 2016).…”

Alternative facts and fake news entering journalistic content production cycle

“…The answer to this question lies in multifold research on automating journalistic content production. The algorithmic news production has been seen as cost-effective and redefining the labor (van Dalen 2012; van der Kaa 2014; Napoli 2014) and for some time was treated as a promising development in news production (Dörr 2015;Clerwall 2014;Carlson 2015). A more critical approach indicates that the objective of information processing will always demand human competence (Linden 2016) and disconnecting journalistic decision-making from the news production process has consequences (Carlson 2017), since algorithmic news production may lead to distribution of fake news.…”