Intrusion of software robots into journalism: The public's and journalists' perceptions of news written by algorithms and human journalists

Jung, Jaemin; Song, Haeyeop; Kim, Young‐Ju; Im, Hyunsuk; Oh, Se-Wook

doi:10.1016/j.chb.2017.02.022

Cited by 80 publications

(37 citation statements)

References 41 publications

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“…On the other hand, they also develop projects to boost their use both in terms of content improvement, increased marketing efficiency and economic feasibility, automation of information validation within the information material, or to speed up the classification of information contained within a massive stream of data [25]. In 2017, Jung et al in the study entitled: Intrusion of software robots into journalism: The public's and journalists' perceptions of news written by algorithms and human journalistspointed out that the audience tended to trust the journalistic materials written by software more than those written by journalists [26].…”

Section: Automation and Newsroomsmentioning

confidence: 99%

Artificial Intelligence and Automated Journalism: Contemporary Challenges and New Opportunities

Ali¹,

Hassoun²

2019

International Journal of Media, Journalism and Mass Communicati

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Section: Automation and Newsroomsmentioning

confidence: 99%

Artificial Intelligence and Automated Journalism: Contemporary Challenges and New Opportunities

Ali¹,

Hassoun²

2019

International Journal of Media, Journalism and Mass Communicati

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“…All focus on quality assessments, which the first three studies operationalize as credibility evaluations. Jung et al (2017) analyzed quality as a broader concept, which includes credibility. For the differing study designs see Supplementary Material A.…”

Section: Previous Research On Credibility Selection and Automated Jmentioning

confidence: 99%

Algorithms in the newsroom? News readers’ perceived credibility and selection of automated journalism

2018

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Automated journalism, the autonomous production of journalistic content through computer algorithms, is increasingly prominent in newsrooms. This enables the production of numerous articles, both rapidly and cheaply. Yet, how news readers perceive journalistic automation is pivotal to the industry, as, like any product, it is dependent on audience approval. As audiences cannot verify all events themselves, they need to trust journalists' accounts, which make credibility a vital quality ascription to journalism. In turn, credibility judgments might influence audiences' selection of automated content for their media diet. Research in this area is scarce, with existing studies focusing on national samples and with no previous research on 'combined' journalism-a relatively novel development where automated content is supplemented by human journalists. We use an experiment to investigate how European news readers (N = 300) perceive different forms of automated journalism in regard to message and source credibility, and how this affects their selection behavior. Findings show that, in large part, credibility perceptions of human, automated, and combined content and source(s) may be assumed equal. Only for sports articles was automated content perceived significantly more credible than human messages. Furthermore, credibility does not mediate the likelihood of news readers to either select or avoid articles for news consumption. Findings are, among other things, explained by topic-specific factors

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“…One strand of research has focused on audiences' opinions about written news texts produced with the help of automation, or labeled as such (Clerwall, 2014;Graefe, Haim, Haarmann, & Brosius, 2016;Graefe, Haim, & Diakopoulos, 2017;Jung, Song, Kim, Im, & Oh, 2017;van der Kaa & Krahmer, 2014;Waddell, 2018;Zheng, Zhong, & Yang, 2018). Most of these studies' findings have shown few, or minor, differences in the way readers perceive human-written and "automated" texts (see, e.g., Clerwall, 2014;van der Kaa & Krahmer, 2014;.…”

Section: Automated Journalismmentioning

confidence: 99%

Computational Journalism

Thurman¹

2019

The Handbook of Journalism Studies

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Computational news gathering and evaluation can utilize tools that find and filter newsworthy information from social media platforms and document caches and that provide guidance on the credibility of content and contributors. Such tools include Dataminr, which promises to deliver "the earliest tips for breaking news" and claims to be used in more than 400 newsrooms around the world (Dataminr, n.d.). Computational news composition and presentation can make use of natural language generation and artificial intelligence to generate written and audiovisual news texts, often from data-feeds. Fanta (2017) found that 9 of the 14mainly Europeannews agencies he surveyed were making use of automated news writing, and two others had projects underway. Examples of the role computing can take in news distribution include automated news personalizationwhere stories are chosen and prioritized according to individual users' explicitly registered and / or implicitly determined preferences-and news aggregation sites and apps, like Google News, whose algorithms "determine which stories, images, and videos [to] show, and in what order" (Google, n.d.). According to Thurman (2011), by 2009 the online editions of a sample of large, legacy news providers in the UK and U.S. all carried a considerable variety of tools to tailor stories to their users' interests. Although some of these practices are not newautomated news personalization dates back to at least the 1980s (Thurman, 2019)it was only from about 2006 that they This is an Accepted Manuscript of a book chapter to be published in Karin Wahl-Jorgensen and Thomas Hanitzsch (Eds.) (2019) The Handbook of Journalism Studies, Second Edition. New York: Routledge. 2 started to be discussed under the single, collective term of computational journalism. This chapter provides a summary of, and commentary on, academic studies focused on computational journalism that were published or presented before August 2018. The search term 'computational journalism' was used to query Google Scholar, and the records returned were reviewed. The process of choosing which of the more than 1000 items to include was necessarily subjective. Given the focus of this handbook, technical works from the computer science domain were mostly excluded, or mentioned in passing, in favor of literature from the sociological and behavioral sciences and the humanities. As will be shown, the focus of computational journalism's literature has broadened over time. An initial emphasis on searching for and analyzing data as part of investigative journalism endeavors has faded as automated news writing, novel forms of interactive news presentation, and personalized news distribution have been addressed. There has also been a growing critical engagement, tempering the early, broadly optimistic analyses with more realistic assessments of computation's effects on the practice of journalism, its content, and reception. The chapter ends with a discussion of how the literature is evolving, addressing new practices-such as "sensor jour...

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Intrusion of software robots into journalism: The public's and journalists' perceptions of news written by algorithms and human journalists

Cited by 80 publications

References 41 publications

Artificial Intelligence and Automated Journalism: Contemporary Challenges and New Opportunities

Artificial Intelligence and Automated Journalism: Contemporary Challenges and New Opportunities

Algorithms in the newsroom? News readers’ perceived credibility and selection of automated journalism

Computational Journalism

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