“Fair and Balanced”: What News Audiences in Four Countries Mean When They Say They Prefer Impartial News

Mont'Alverne, Camila; Badrinathan, Sumitra; Arguedas, Amy A. Ross; Toff, Benjamin; Fletcher, Richard; Nielsen, Rasmus Kleis

doi:10.1080/1461670x.2023.2201864

Cited by 8 publications

(3 citation statements)

References 36 publications

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“…Finally, relying on cues for trust does not mean other factors are unimportant. We found evidence of widespread skepticism among many participants (Mont'Alverne et al, 2023), often anchored in pervasive negative ideas about the news media, which are often the focus of other research on trust in news. These unfavorable views are a salient backdrop for how many experience news abundance online.…”

Section: Discussionmentioning

confidence: 64%

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Shortcuts to trust: Relying on cues to judge online news from unfamiliar sources on digital platforms

Ross Arguedas,

Badrinathan,

Mont’Alverne

et al. 2023

Journalism

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Scholarship has increasingly sought solutions for reversing broad declines in levels of trust in news in many countries. Some have advocated for news organizations to adopt strategies around transparency or audience engagement, but there is limited evidence about whether such strategies are effective, especially in the context of news consumption on digital platforms where audiences may be particularly likely to encounter news from sources previously unknown to them. In this paper, we use a bottom-up approach to understand how people evaluate the trustworthiness of online news. We inductively analyze interviews and focus groups with 232 people in four countries (Brazil, India, the United Kingdom, and the United States) to understand how they judge the trustworthiness of news when unfamiliar with the source. Drawing on prior credibility research, we identify three general categories of cues that are central to heuristic evaluations of news trustworthiness online when brands are unfamiliar: content, social, and platform cues. These cues varied minimally across countries, although larger differences were observed by platform. We discuss implications of these findings for scholarship and trust-building efforts.

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Section: Discussionmentioning

confidence: 64%

“…The more you have of those, the less trustworthy it is." As these quotes suggest, underpinning participants' attention to sensational language was concern about hidden motives behind news (Mont'Alverne et al, 2023).…”

Section: Content Cuesmentioning

confidence: 99%

Shortcuts to trust: Relying on cues to judge online news from unfamiliar sources on digital platforms

Ross Arguedas,

Badrinathan,

Mont’Alverne

et al. 2023

Journalism

Self Cite

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“…Despite negative effects associated with disclosure about the use of AI, we predict that attitudes may be conditional on two factors. First, given that skepticism about the inherent subjectivity of journalists is likely highest among those who are distrusting of news in general (Mont'Alverne et al, 2023;Ojala, 2021), we theorize that prior levels of trust in news will also shape the way audiences perceive disclosures about the use of AI in non-linear ways. Specifically those who are least trusting may be most favorably predisposed to see advantages from "mechanical accuracy" as an alternative to the judgments of professional journalists.…”

Section: Hypotheses and Research Questionsmentioning

confidence: 99%

“Or they could just not use it?”: The Paradox of AI Disclosure for Audience Trust in News

Toff,

Simon

2023

Preprint

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The adoption of artificial intelligence (AI) technologies in the production and distribution of news has generated theoretical, normative, and practical concerns around the erosion of journalistic authority and autonomy and the spread of misinformation. With trust in news already low in many places worldwide, both scholars and practitioners are wary of how the public will respond to news generated through automated methods, prompting calls for labeling of AI-generated content. In this study, we present results from a novel survey-experiment conducted using actual AI-generated journalistic content. We test whether audiences in the US, where trust is particularly polarized along partisan lines, perceive news labeled as AI- generated as more or less trustworthy. We find on average that audiences perceive news labeled as AI-generated as less trustworthy, not more, even when articles themselves are not evaluated as any less accurate or unfair. Furthermore, we find that these effects are largely concentrated among those whose pre-existing levels of trust in news are higher to begin with and among those who exhibit higher levels of knowledge about journalism. We also find that negative effects associated with perceived trustworthiness are largely counteracted when articles disclose the list of sources used to generate the content. As news organizations increasingly look toward adopting AI technologies in their newsrooms, our results hold implications for how disclosure about these techniques may contribute to or further undermine audience confidence in the institution of journalism at a time in which its standing with the public is especially tenuous.

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Towards a taxonomy of English-language news podcasting in India

Mehendale

2024

Media Asia

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“Fair and Balanced”: What News Audiences in Four Countries Mean When They Say They Prefer Impartial News

Cited by 8 publications

References 36 publications

Shortcuts to trust: Relying on cues to judge online news from unfamiliar sources on digital platforms

Shortcuts to trust: Relying on cues to judge online news from unfamiliar sources on digital platforms

“Or they could just not use it?”: The Paradox of AI Disclosure for Audience Trust in News

Towards a taxonomy of English-language news podcasting in India

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