Automated News

A Semantic Preprocessing Framework for Breaking News Detection to Support Future Drone Journalism Services

Nowadays, news coverage implies the existence of video footage and sound, from which arises the need for fast reflexes by media organizations. Social media and mobile journalists assist in fulfilling this requirement, but quick on-site presence is not always feasible. In the past few years, Unmanned Aerial Vehicles (UAVs), and specifically drones, have evolved to accessible recreational and business tools. Drones could help journalists and news organizations capture and share breaking news stories. Media corporations and individual professionals are waiting for the appropriate flight regulation and data handling framework to enable their usage to become widespread. Drone journalism services upgrade the usage of drones in day-to-day news reporting operations, offering multiple benefits. This paper proposes a system for operating an individual drone or a set of drones, aiming to mediate real-time breaking news coverage. Apart from the definition of the system requirements and the architecture design of the whole system, the current work focuses on data retrieval and the semantics preprocessing framework that will be the basis of the final implementation. The ultimate goal of this project is to implement a whole system that will utilize data retrieved from news media organizations, social media, and mobile journalists to provide alerts, geolocation inference, and flight planning.

A systematic review of automated journalism scholarship: guidelines and suggestions for future research

Background: The use of advanced algorithmic techniques is increasingly changing the nature of work for highly trained professionals. In the media industry, one of the technical advancements that often comes under the spotlight is automated journalism, a solution generally understood as the auto generation of journalistic stories through software and algorithms, without any human input except for the initial programming. Methods: In order to conduct a systematic review of existing empirical research on automated journalism, I analysed a range of variables that can account for the semantical, chronological and geographical features of a selection of academic articles as well as their research methods, theoretical backgrounds and fields of inquiry. I then engaged with and critically assessed the meta-data that I obtained to provide researchers with a good understanding of the main debates dominating the field. Results: My findings suggest that the expression "automated journalism" should be called into question, that more attention should be devoted to non-English speaking scholarship, that the collective and individual impacts of the technology on media practitioners should be better documented and that well-established sociological theories such as institutionalism and Bourdieu's field theory could constitute two adequate frameworks to study automated journalism practices. Conclusions: This systematic literature therefore provides researchers with an overview of the main challenges and debates that are occurring within the field of automated journalism studies. Future studies should, in particular, make use of institutionalism and field theory to explore how automated journalism is impacting the work of media practitioners, which could help unearth common patterns across media organisations.

A Decision-Making Model for Adopting Al-Generated News Articles: Preliminary Results

This study was conducted to present a consumer acceptance model for artificial intelligence (AI)-generated news articles in review of the most significant factors in the context of accepting AI-generated news articles. To this end, a survey was conducted regarding five key factors among individuals handling news production at media organizations who were familiar with AI-related news: benefits of technology utilization, recognized values, media reliability, content quality, and information perception. These factors were classified into 19 specific evaluation items in order to determine relative importance. According to the results of pairwise comparison analysis among elements through the analytic hierarchy process, hereunder referred to as AHP (analytic hierarchy process), it turned out that the importance of media reliability and content levels was relatively high, while the importance of recognized values and information perception was relatively low. It also turned out that among 19 specific items, the importance of institutional reliability, which was a subordinate item under media reliability, was of utmost value. Even if news articles were based on AI technology, their readers were more likely to be affected by reliability, which is a major attribute of journalism, than technical factors.

Algorithmic Actants in Practice, Theory, and Method

What changes as algorithms proliferate within journalism and become more sophisticated? In this essay, we synthesize the articles in this thematic issue, which offer empirical evidence for how algorithms—and especially those designed to automate news production—are being incorporated not only into journalistic activities but also into the logics of journalism itself. They underscore that journalists have neither feared nor rejected such algorithms, as might be expected given the recent history of technological adoption in journalism. Instead, journalists have sought to normalize the technology by negotiating them against existing values and practices, and perhaps even reified some normative ideological constructs by finding unique value in what they offer as humans. These articles also highlight the shortcomings of those algorithms, giving pause to postulations of algorithms as potential solutions to shortcomings of trust in news and market failures. Indeed, such algorithms may end up amplifying the very biases that seed distrust in news all the while appearing less valuable to readers than their human counterparts. We also point to new opportunities for research, including examinations of how algorithms shape other stages in the journalistic workflow, such as interviewing sources, organizing knowledge, and verifying claims. We further point to the need to investigate higher analytic levels and incorporate additional perspectives, both from more diverse contexts (e.g., Global South) and from our sister academic fields (e.g., human–computer interaction). We conclude with optimism about the continued contributions this stream of work is poised to make in the years to come.