Considering the different expectations that pervade journalism practice, this article aims to understand how discrepancies between the audiences' and journalists' perceptions of journalistic roles might affect journalistic authority in Italy. More specifically, we stem from data collected through two simultaneous surveys, one with citizens and another with journalists, to grasp (1) if the public and press professionals have different assessments on journalistic roles; and (2) whether these discrepancies affect citizens' perceptions of journalistic authority. Results demonstrate that, although both groups have similar patterns concerning their general expectations on journalistic activity, they also differ significantly. While journalists value more aspects related to the monitorial role, Italian citizens also seem to expect a more engaged attitude from the press, giving more importance than news professionals to how journalism may support governments and development. We found that these discrepancies affect journalistic authority in a nuanced way: legitimacy is undermined when journalists and the public disagree about the monitorial role, but rises when the same happens concerning a more partisan view of journalism. Although these findings align with characteristics of the Italian media system, we argue that they also have broader implications for research on journalistic authority, reinforcing the need for more audience-centered investigations.
Personalization driven by generative artificial intelligence (GenAI) has progressively permeated newswork operations, encompassing news content generation, presentation, distribution, and communicative interaction across four aspects. However, whether these innovations attract the public, and what factors count are far from settled. Utilizing cross-national data from the Digital News Report (2025) and the AI Governance International Evaluation Index (2025), this study explores the extent and key predictors of public interest in GenAI-driven journalism innovations for personalization across 34 countries worldwide (N = 54,799). Results show that public interest is moderate overall, with GenAI-driven news summaries being the most favored. At the individual level, news consumption patterns, both active news seeking and incidental news exposure, emerge as positive determinants. At the societal level, AI governance capacity matters more than AI infrastructural transparency, as it operates not only as a direct predictor but also as a contextual condition that amplifies or mitigates the effect that news use patterns have on people's enthusiasm. Theoretical and practical implications are discussed.
News coverage of cross-border conflict is an essential element of international discourse, constructing national and social identities for global publics. Most journalism studies of border violence have focused on conflict among nations at war. Rarer studies of "friendly" nations have highlighted only news coverage within the dominant nation. This study builds on existing knowledge of journalistic discourse through a cross-national analysis that examines news coverage of the killings of Bangladeshi citizens by Indian Border Security Forces, as reported in major newspapers in both countries. We find that newspapers in the dominant country (India) mostly ignore such extrajudicial killings and vilify the victims, perhaps in line with Indian national interests. Meanwhile, newspapers in the weaker nation (Bangladesh) also downplay the violence through episodic reporting and reliance on official sources. The Bangladeshi coverage, therefore, reflects a more complex and ambivalent orientation, exposing a disconnect between the interests of its government and its citizens. The findings suggest that national political agendas and professional news routines lead on both sides to the naturalization of such violence, at the expense of human rights.
Drawing on Expectancy Violations Theory (EVT), we conducted 60 semi-structured interviews to examine (1) how news audiences in Australia and Germany form expectations about journalists' use of generative AI (GenAI); (2) the antecedents shaping those expectations; and (3) how they evaluate perceived expectation violations. Participants envisioned journalism as a human-centered practice, rejecting autonomous GenAI use in core reporting functions. Transparency, disclosure, and oversight (whether regulatory or newsroom-led) were treated as conditions of legitimacy. Expectations were shaped by institutional trust, everyday experiences with AI, and broader reputations of AI technologies. Participants described negative violations linked to perceived ethical lapses, though some reported positive violations when AI enhanced accessibility or efficiency. Our findings demonstrate how audience responses to GenAI are not merely technological assessments, but relational evaluations grounded in normative expectations about journalism.
How do audiences respond to AI-authored news content? This study draws on Source Credibility Theory, the Heuristic-Systematic Model (HSM), and the Elaboration Likelihood Model (ELM) to examine how source attribution (AI vs. human journalist) and news topic (economy vs. immigration) affect perceived news credibility. It also explores whether AI familiarity and personal topic involvement moderate this relationship, and whether perceived credibility predicts future AI news selection. A preregistered 2 (source: AI vs. human journalist) & times; 2 (topic: economy vs. immigration) between-subjects experiment (N = 1,012) was conducted. Results show no significant interaction between source and topic on credibility ratings, and neither AI familiarity nor personal involvement moderated this relationship. However, credibility perceptions were significantly associated with future AI news selection among participants with high topic involvement. These findings suggest that readers often overlook or disregard source attribution, possibly reflecting a broader normalization of AI authorship in news. Rather than source cues, personal involvement with the issue emerged as a more influential factor in shaping future engagement with AI-generated news. The study highlights the need for more effective strategies to signal AI authorship and informs ongoing debates about transparency and trust in AI-powered journalism.
While social media seem to have decentralized online discussions and interactions by enabling crowds and crowdsourcing activities, recent studies show that elite actors - news media and government - still dominate networked framing processes. As political and platform control have heightened in both autocracies and democracies recently, framing power seems to have increasingly shifted to elites. This study, therefore, seeks to explore the extent and how crowds and crowd dynamics still shape networked framing under restrictive information environments. Combining network and frame analysis with qualitative interviews to examine online discussions over a major wildfire in China, this study finds novel forms of crowd-elite dynamics vis-& agrave;-vis distinct professional visions of frame building and dissemination. Driven by the professional claim of "truth," Chinese state/media tried to exclude popular engagements while enlisting peer state/media to propagate state-authenticated "truth" frames, i.e., in an elite-driven crowdsourcing model. Propelled by the professional ideal of "public interests," market-oriented local digital media incorporated crowd-publics in their content production, whereas crowds reciprocally diffused such publicly oriented frames in an elite-public collaboration model. As information controls have intensified globally, this study aims to inform framing processes in similar information-restrictive environments by unraveling new patterns of crowd engagement and crowd-elite interactions.
Many studies in the field of science journalism focus on the challenges of producing science news in the contemporary media ecology, but relatively few studies address issues related to the emotional wellbeing of journalists and scientists. In this survey-based study, we analyze which factors are associated with burnout in journalists who write about science and scientists who have served as interview subjects for journalists. We find that overwork and negative work experiences may be associated with higher reports of burnout, but resilience may interrupt these associations. These results may have implications for the development of wellbeing interventions for journalists and scientists.
As artificial intelligence-generated (AI-generated) journalism rises, its effectiveness in engaging the public to mitigate climate change remains uncertain. This study integrates the MAIN model and expectation-confirmation theory to examine how AI-generated versus human-generated climate change news influences audience news evaluation and behavioral intentions. We conducted a 2 (human-generated news vs. AI-generated news) & times; 2 (authorship disclosed vs. authorship undisclosed) & times; 2 (narrative vs. non-narrative) between factorial experiment (N = 441) to test the effects of news source, authorship disclosure, and narrative style on readers' perceived news credibility, readability, and behavioral intentions for climate change mitigation. The t-tests revealed that human-generated climate change news elicited positive disconfirmation, while AI-generated news resulted in negative disconfirmation. A three-way MANCOVA revealed that narrative news significantly promoted climate change mitigation intentions. Further moderation analyses indicated that while AI-generated news did not differ significantly from human-generated news in terms of persuasiveness, improving the algorithmic narrative structure of AI-generated content could enhance positive disconfirmation of readability, thereby increasing the likelihood of engagement in mitigation behaviors. These findings provide insights into optimizing AI-generated journalism to enhance climate change communication and public engagement.
This article responds to Andrea Wenzel and Claire Wardle's commentaries on the provocation "Constructive Research: Making Journalism Research Matter." Engaging with their critiques, the reply revisits the constructive research continuum and reflects on three interconnected issues raised by the respondents: the historical precedents of constructive research, the institutional and structural conditions shaping journalism researchers' role conceptions, and the normative questions surrounding neutrality and objectivity in more engaged forms of scholarship. Drawing on traditions within journalism studies-including research on professional roles and influencing factors-the article argues that the field already possesses conceptual resources for further theorizing the roles of journalism researchers. Building on James W. Carey's distinction between models "of" and "for" communication, the article suggests that models developed to study journalism may also serve as tools for reflecting on scholarly practice itself. The reply concludes by proposing a process of "reflexive mirroring," in which journalism research is turned inward to critically examine the norms, practices, and roles of journalism researchers.
The term "data journalism" has become a buzzword in contemporary newsrooms, yet studies report varying levels of its adoption. This study introduces a new analytical model-the data frame framework-to explain how journalists' sense-making shapes the adoption or resistance of data journalism practices (DJP). Drawing on 27 in-depth interviews conducted in a major Israeli news organization, the study identifies distinct interpretive frames through which reporters, editors, designers, and managers understand DJP, including its perceived value, relevance, and feasibility. Moving beyond the conventional focus on structural economic or technical barriers, the framework reveals the cognitive dynamics that influence whether DJP becomes embedded or sidelined within professional routines. By showing how divergent frames generate friction across roles and departments, the study deepens understanding of the organizational and professional tensions surrounding DJP and underscores the need for shared sense-making processes to support its sustainable integration.
