doxa.comunicación | nº 29, pp. 275-286 | 277

July-December of 2019

Jesús Segarra-Saavedra, F. J. Cristòfol and Alba-María Martínez-Sala

ISSN: 1696-019X / e-ISSN: 2386-3978

1.1. Investigation of AI applied to journalism.

It is possible to increase and speed up automatically generated news from structured data, artificial intelligence, and the internet in the era of infoxification, big data, algorithms, and the rest of the new current and future technologies. Bots, which are also known as software robots, are gradually being incorporated into journalism. Since contents generated by automatic and automated software are being implemented, we are prompted to rethink and reformulate the future of the profession of journalism,

Despite the many advantages (speed, economy, and reduction in human errors), this reality should be approached through the balance between the business perspective (profitability) and the human perspective (social and perceptive). In this sense, it is pertinent to know who the primary reference researchers are in regards to AI applied to journalism.

Table 1: Principal investigators of artificial intelligence (AI) applied to journalism.

Source: Author-created

One of the first approaches to the phenomenon of the present study was made by Glahn (1970), who investigated the area of programming in which weather forecasts are created for information purposes. Shortly thereafter, Lee & Kim (1998) analyzed the on-demand news service system that gathers daily information from different sources through a robot that provides news oriented towards its users according to their preferences and interests. Thus, segmented news began to be generated and disseminated to specific audiences.

Van-Dalen (2012) set out to investigate audiences’ reactions to sports news. In this sense, his contributions helped jour-nalists to identify the areas in which they have to acquire new skills, such as analytical skills, personality, creativity, and the ability to write linguistically complex sentences. The study concluded that journalists view robot journalism as an opportunity to humanize journalism, while it noted that the greater the automation, the greater the time investment in investigating the facts. Therefore, they did not find a temporary saving.

Graefe’s contributions (2016) allowed the state of automated journalism to advance. Thanks to the productivity study of the use of algorithms for automatically creating news from semi-structured data, he was able to find how this also affects its speed, economic profitability, and its potential to reduce human error significantly.