The analysis, published in Frontiers in Communication, combined AI-assisted filtering with human validation, multilabel thematic coding and statistical testing of user responses. GPT-4o mini was used to identify potentially relevant posts and later classify replies, but researchers manually reviewed every flagged post, independently reassessed ambiguous cases and audited a random 10% of the rejected material. Two researchers coded the thematic content, with a blind 50-post overlap producing a Cohen’s kappa of 0.72 and observed agreement of 0.83. The study is observational and describes the content and reception of posts; it does not establish that Musk’s rhetoric caused changes in public trust, political behaviour or the quality of information.

Musk’s most frequent references were to broad categories such as “media,” “legacy media,” “mainstream media” and “press,” rather than to individual journalists or isolated reporting errors. Generic labels enabled broad claims about bias, political coordination and informational manipulation, while identifiable outlets supplied concrete examples for those claims. X was mentioned more often than Facebook, LinkedIn or YouTube. The identifiable media organisations were also geographically concentrated: 153 references concerned United States outlets and 42 British outlets, while continental European organisations appeared much less frequently. The corpus therefore captures a discourse centred primarily on Anglo-American media conflicts during a period of intense electoral polarisation.

Bias and credibility criticism dominated the thematic analysis, appearing in 59.2% of the 500 relevant posts. Journalism was represented as selective, dishonest, partisan or structurally incapable of neutrality, rather than merely occasionally fallible. Accusations of disinformation and propaganda appeared in 35% of posts, often depicting media organisations as intentionally concealing information, coordinating messages or manipulating elections. Claims about electoral interference appeared in 13.4%. Against this construction, X was associated with participation, direct communication and freedom from institutional filters. Musk’s statement after the election that “You are the media now” exemplified the inversion: instead of journalists distributing information to an audience, platform users were presented as journalists capable of producing and circulating it themselves.

The analysis identified a clear normative asymmetry between journalism and X. References to traditional media were dominated by delegitimising frames involving manipulation, propaganda, bias and deception, whereas references to X were linked to openness, participation, innovation and communicative freedom. Technological transformation and media innovation occurred in 40.2% of X-related discourse but only 7.6% of media-related discourse. Disinformation was framed in the opposite directions: media organisations appeared as producers and distributors of distorted narratives, while X was portrayed as a corrective environment in which users could challenge reporting. Even humour served contrasting purposes, mocking media organisations through satire in one case and celebrating virality and citizen participation in the other.

Citizen journalism nevertheless did not eliminate mediation in the platform model. Musk’s rhetoric contrasted professional and editorial selection with platform-based mechanisms such as user participation, rebuttals and Community Notes. But X remained an algorithmically organised infrastructure that selected, ordered and distributed content, and Musk controlled the platform promoting itself as a substitute for legacy media. The study therefore interprets “cutting out the middleman” as a relocation rather than the removal of mediation. The locus of gatekeeping shifts from professional journalists toward users and platform systems, while the owner of that system supplies the normative account of the shift and retains control over the infrastructure on which it occurs.

User responses broadly converged with Musk’s position without producing a uniformly supportive discussion. Researchers collected 42,066 replies through stratified sampling from 3,233,112 interactions associated with the selected posts. The model classified 25,520 replies, or 60.67%, as agreement and 15,342, or 36.47%, as disagreement; 2.86% lacked a sufficiently identifiable evaluative position. Support was especially concentrated around posts alleging political and electoral manipulation or attacking media bias and credibility. Posts presenting X as a direct alternative and discussing technology, artificial intelligence or communication transformation showed relatively more disagreement, suggesting that rejection of institutional journalism was more mobilizing than enthusiasm for the proposed platform substitute.

The statistical analysis found statistically significant associations between agreement and both thematic orientation and inferred professional background, but their effect sizes were limited. Cramér’s V was 0.0485 for thematic categories and 0.1100 for professional categories. Inferred gender was not significantly associated with agreement at the conventional threshold: the p-value was 0.0620 and the effect size was only 0.0144. Professional inference was possible for 7,043 profiles, while 81.73% of the sample lacked sufficient occupational information. Profiles linked with design and creative work, research, activism and socio-political analysis tended toward greater disagreement, whereas some managerial, family-oriented, retirement-related and property-associated profiles showed relatively more agreement. These patterns cannot establish the identities or occupations of respondents.

Several constraints make the findings narrow but informative. The study covers one highly polarised political transition rather than Musk’s wider record or X users in general. It measures what appeared in a sampled set of posts and replies, not the truth of claims about media bias, propaganda or electoral manipulation. Gender and profession were estimated from self-presentational profile text, and automated classification can misread irony, slang, memes, evolving jargon and context-dependent remarks. Researchers addressed these issues through manual review and reclassification, but some interpretive uncertainty remains. The central result is a description of rhetorical organization: Musk framed journalism as politically compromised and X as a corrective, participatory space, while users generally endorsed the criticism more strongly than the alternative, and the platform’s owner simultaneously promoted and governed the mechanism presented as replacing professional mediation.

Subject of Research: Analysis of Elon Musk’s anti-media and citizen-journalism rhetoric on X during the 2024 US presidential transition

Article Title: Cutting out the middleman? Elon Musk and the rhetoric of citizen journalism in platform-mediated communication

Article References: Peña Alonso, U., Meso Ayerdi, K., Peña Fernández, S., & Morales i Gras, J. (2026). Cutting out the middleman? Elon Musk and the rhetoric of citizen journalism in platform-mediated communication. Frontiers in Communication, 11, Article 1901417. https://doi.org/10.3389/fcomm.2026.1901417

Image Credits: AI Generated

DOI: 10.3389/fcomm.2026.1901417

Keywords: Elon Musk, X platform, citizen journalism, anti-media rhetoric, media legitimacy, platform governance, information authority, social media analysis, AI-assisted research, Cutting, middleman, Elon

The concept of communicative power offers a useful lens for interpreting what happened in the county. Drawing on networked theories of communication, the authors argue that decentralized digital networks can outflank institutional gatekeepers who traditionally decided which environmental issues deserved public attention. In practice, this meant that a resident of Kositei or Bartabwa who witnessed a drying spring or illegal logging could document it with a mobile phone and circulate the account through Facebook or WhatsApp, bypassing editors in Nairobi. This does not mean traditional gatekeeping disappeared. Mainstream journalists in Kenya still controlled the largest audiences and retained considerable authority, but the terms of engagement shifted: reporters increasingly found story leads, images, and testimony originating from citizen producers. In agenda-setting terms, ordinary people gained a capacity to place local ecological concerns before the very institutions that had historically overlooked them, which the study identifies as the central reconfiguration of the period.

The finding that citizen journalism in Baringo emerged first as a survivalist strategy carries significant theoretical weight. Much of the international scholarship celebrates citizen journalism as an expression of democratic participation or watchdog accountability. In Baringo, however, the earliest digital reporting practices arose from immediate practical needs: communities needed to warn one another about drought conditions, coordinate responses to water scarcity, and mobilize around threats posed by deforestation and the expansion of geothermal infrastructure. Communication functioned, above all, as a tool for collective survival in an environment where ecological stress translated directly into threats to livelihoods, livestock, and safety. This reframing suggests that scholars examining citizen journalism in vulnerable regions should pay closer attention to the material stakes of environmental information, rather than assuming that participation is driven primarily by civic ideals or professional aspiration.

The coexistence of digital platforms with radio deserves particular emphasis, because it complicates popular narratives of wholesale technological displacement. Across the interviews and focus group discussions conducted in locations such as Barwesa, Kinyach, Maji Moto, Solian, Mogotio, and Kabartonjo, radio remained the most trusted source of environmental information, especially for older residents and those without reliable connectivity. Younger and more digitally literate community members increasingly turned to mobile phones and social media, but these channels often worked in tandem with broadcast media rather than against it. A report circulating on WhatsApp could be amplified when a radio station picked it up, and radio bulletins could spark digital discussion among dispersed family and community networks. This layered media ecology reflects what communication scholars describe as media hybridity, where older and newer forms interpenetrate, each compensating for the limitations of the other in specific social and infrastructural contexts.

The interpretation offered by the study is explicitly interpretivist, and this methodological stance shapes its conclusions in important ways. Rather than treating technology diffusion as a uniform process that unfolds identically across regions, the researchers approached each community’s adoption of digital tools as embedded in local meanings, histories, and social relations. The oral interviews with elders, activists, journalists, and civil society leaders revealed that the significance of a mobile phone in a semi-arid village differed markedly from its significance in an urban newsroom. For irrigation farmers and digital content creators participating in focus groups, digital communication was tied to advocacy strategies and market awareness; for elders, it was often mediated through younger relatives who translated and relayed information. This variability cautions against policy frameworks that assume a single national trajectory of digital transformation and instead supports locally attuned interventions that recognize differentiated patterns of use.

Infrastructural inequality emerged as one of the most persistent constraints on inclusive participation. Weak internet access, unreliable electricity, and uneven mobile network coverage meant that the benefits of digital environmental reporting accrued unevenly within the county. Communities closer to towns or along better-served corridors could participate more consistently in digital discourse, while residents of remote settlements remained dependent on intermittent connectivity or on secondhand relay through radio and word of mouth. The study’s evidence therefore aligns with a substantial body of research on the digital divide, which emphasizes that access is not merely a matter of device ownership but of the reliability, affordability, and quality of connectivity. In Baringo, the divide did not prevent citizen journalism from emerging, but it structured who could speak, how often, and with what visibility—a stratification that has consequences for whose environmental grievances reach policymakers.

Digital illiteracy constituted a second barrier closely intertwined with the first. Even where connectivity existed, the skills required to produce, verify, and circulate credible environmental reports were unevenly distributed, often following generational and educational lines. The study suggests that this skills gap limited the depth of participation for many residents, confining them to the role of consumers rather than producers of digital content. Related to this was the problem of misinformation, which the authors identify as a genuine cost of decentralized reporting. Without editorial verification mechanisms, inaccurate claims about ecological events could spread rapidly through social networks, potentially eroding trust or prompting ill-founded responses. The Baringo experience thus illustrates a dilemma widely discussed in environmental communication research: the same openness that enables marginalized communities to report on their own conditions also removes the quality controls that institutional journalism, whatever its biases, traditionally provided.

The environmental issues documented through these digital channels—drought, deforestation, water scarcity, and geothermal expansion—reflect the distinctive pressures confronting Kenya’s Rift Valley in this period. Geothermal development, in particular, represents a compelling case of how citizen journalism reframed contested development. While geothermal energy is often framed nationally as a clean and progressive resource, communities hosting such infrastructure experienced land acquisition, environmental disturbance, and questions about benefit-sharing. Digital platforms gave affected residents a means to document these tensions and insert them into public debate, illustrating how environmental communication can mediate between national development narratives and local experience. Similarly, reports of drought and water scarcity generated by citizens provided granular, real-time documentation that formal monitoring systems, constrained by limited coverage in remote areas, often could not match in timeliness.

The temporal framing of the study, ending in 2015, is deliberate and analytically significant. The authors argue that the practices consolidated during this five-year window provided a foundation for post-2015 environmental activism in Kenya’s semi-arid regions. The period corresponds with rapid growth in mobile penetration across East Africa and with the maturation of social media platforms as mass communication channels in Kenya. By the middle of the decade, the habit of documenting environmental grievances digitally had moved from novelty to normalized practice, and a generation of digitally engaged residents had acquired skills and networks that later advocacy would build upon. In this sense, the 2010–2015 period should be understood less as a completed transformation than as a formative phase in which the groundwork for subsequent mobilization was laid under conditions of considerable constraint.

Finally, the study makes a valuable contribution simply by redirecting scholarly attention toward rural semi-arid Kenya, a setting that remains underrepresented in digital media research dominated by studies of Nairobi and other urban centers. The Kenyan case demonstrates that the democratizing effects of digital communication are real but conditional: they depend on infrastructure, literacy, trust in media forms, and the historical legacies of marginalization that shape who participates and on what terms. For researchers, the Baringo record argues for historical and ethnographic approaches that track how communities themselves adopt, adapt, and repurpose technologies. For practitioners and policymakers, it suggests that supporting environmental communication in marginalized regions requires investing in connectivity and digital skills alongside, rather than in place of, trusted channels such as radio. The reconfiguration of communicative power that the authors document was partial and uneven, yet it fundamentally altered who could speak about the environment and whose concerns counted in public life.

Subject of Research: The role of digital media and citizen journalism in environmental reporting in Baringo County, Kenya, from 2010 to 2015

Article Title: Digital media and citizen journalism in environmental reporting in Baringo County-Kenya, 2010–2015

Article References: Kemei, J. N., Okoth, P. G., & Lusambili, K. M. (2026). Digital media and citizen journalism in environmental reporting in Baringo County-Kenya, 2010–2015. Frontiers in Sustainable Resource Management, 5, Article 1915428. https://doi.org/10.3389/fsrma.2026.1915428

Image Credits: AI Generated

DOI: 10.3389/fsrma.2026.1915428

Keywords: citizen journalism, digital media, environmental communication, Baringo County, Kenya, social media, agenda-setting, digital divide, drought, geothermal expansion, environmental activism, rural media

The study’s central insight is methodological as much as theoretical. Rather than asking people to rate one AI article against one human article in a laboratory setting, Barnidge and Nah conducted a two-wave online survey of 942 U.S. adult internet users and then applied a statistical technique known as bifactor modeling. This approach allows researchers to separate the variance that people’s credibility judgments share across all types of news sources from the variance that is unique to each specific source. In practical terms, the model can distinguish between a person’s general orientation toward news credibility and their particular skepticism or trust toward AI-generated news, professional journalism, or citizen-produced news. The bifactor model fit the data well, with a comparative fit index of 0.94, a Tucker-Lewis index of 0.94, a root mean square error of approximation of 0.058, and a standardized root mean square residual of 0.045, all of which indicate an acceptable fit by conventional psychometric standards.

Why does this decomposition matter? Because, as the authors show, the competing explanations for the AI-journalist credibility gap operate at different levels of the judgment process. When the researchers examined what predicted people’s generalized credibility orientations, the strongest associations were communicative factors: how much exposure people had to AI-related news and how much they discussed such news with others. In other words, people who encounter and talk about stories concerning artificial intelligence develop broad dispositions about whether news in general can be believed. These everyday communicative habits, the study suggests, shape a kind of baseline skepticism or trust that colors judgments of all news sources, whether the byline belongs to a person or an algorithm.

When it came to the specific difference between AI-generated and professional news, however, a different predictor dominated. Trust in journalists was the strongest predictor of the source-specific gap between machine-written and professionally produced news, with a standardized coefficient of 0.25, statistically significant at the p < 0.001 level. This is a striking result. It means that the more people trust journalists as a professional group, the more they differentiate in favor of professional journalism when comparing it against AI-generated alternatives. The gap, in this sense, is not primarily driven by attitudes toward artificial intelligence at all. It is driven by the institutional authority that audiences attach to journalism as a profession, an authority that machine authorship simply does not carry.

To probe this further, the researchers decomposed the professional-AI credibility gap into its component parts, comparing it against the gap between professional and citizen-produced news. The result was revealing: slightly more than half of the professional-AI gap corresponded to the professional-citizen component. This suggests that audiences perceive AI-generated news in a way that is partly analogous to citizen journalism, as news produced outside the boundaries of the professional field. The AI-journalist credibility gap, therefore, reflects two distinct things at once: the simple fact of human versus non-human authorship, and the institutional authority, the norms, routines, and legitimating apparatus, that professional journalism has accumulated over more than a century. AI-generated news suffers not merely because a machine wrote it, but because it lacks the institutional stamp that professional newsrooms confer.

These findings arrive at a moment when the use of AI in news production is both widespread and largely invisible to audiences. Recent research cited in the study, including a 2025 analysis of American newspapers, found that AI use in newsrooms is common, unevenly distributed, and rarely disclosed to readers. Meanwhile, a growing body of experimental work has documented how machine attribution affects perception. Studies dating back to Clerwall’s 2014 experiment on robot journalism, and extended by meta-analyses of machine authorship effects, have shown that readers often rate automated content as more objective but less credible, less trustworthy, and less able to convey expertise or nuance. Other work has invoked the machine heuristic, the cognitive shortcut by which people evaluate machine-produced content differently from human-produced content, and has even found that machine-written political news can intensify hostile media perceptions among partisans.

What the new study adds to this literature is a structural explanation. Previous experiments established that the gap exists; Barnidge and Nah explain where the gap comes from in the architecture of audience judgment. By separating generalized from source-specific variance, the study shows that the credibility deficit of AI news is not simply a matter of novelty effects or technophobia that might fade as AI becomes familiar. Communicative factors, including AI-related exposure and discussion, do shape generalized credibility orientations, but the specific preference for professional over machine-written news is anchored in trust in journalists themselves. That anchoring is likely to be durable, because it rests on institutional legitimacy rather than on fleeting attitudes toward a new technology.

The theoretical implications extend beyond journalism. The authors argue that their findings refine credibility theory by demonstrating that audience evaluations of emerging media are tied not only to attitudes toward new technologies but also to the institutional authority audiences attach to established media institutions. This reframing connects the study to a broader scholarly conversation about metajournalistic discourse and boundary work, the processes by which journalism defines itself and legitimizes its authority in the digital era. It also echoes earlier research on citizen journalism, which found that social trust and perceptions of journalistic roles shape how audiences evaluate non-professional news sources. AI-generated news, in this framework, occupies a position of peripherality in the journalistic field, much like citizen journalists and other new actors that have challenged the boundaries of professional practice.

For the news industry, the practical stakes are considerable. As publishers increasingly deploy large language models to draft routine stories, summarize events, and personalize content, they face a credibility problem that disclosure alone may not solve. If audiences’ preference for professional news stems from institutional trust rather than from ignorance of AI’s capabilities, then simply telling readers that a story was AI-assisted will not close the gap. Conversely, the finding that slightly more than half of the gap corresponds to the professional-citizen component hints that hybrid forms of human-AI collaboration, in which professional journalists supervise and validate machine output, might recapture some of the institutional authority that pure automation lacks. Recent work on visualizing human-AI collaboration disclosures in news production suggests that newsrooms are already experimenting with how to communicate such arrangements to audiences.

The study is not without limitations inherent to its design. It relies on self-reported survey data from U.S. adult internet users, and generalized credibility judgments measured in a survey may not perfectly capture how people react to specific articles in the wild. The bifactor approach, while powerful, requires careful interpretation, and the authors note that regression among factor scores involves well-known statistical subtleties. Still, the two-wave design and the strong model fit lend considerable weight to the conclusions. And the core message is one that both technologists and journalists should heed: the battle for trust in AI-generated news will not be won or lost on the merits of the technology itself. It will be won or lost in the contested terrain of institutional authority, where audiences decide whether the institutions behind the news, human or machine, deserve their belief. As AI-generated content becomes indistinguishable from professional reporting in style and accuracy, that institutional dimension may prove to be the last, and most decisive, frontier of the credibility debate.

Subject of Research: Audience credibility judgments of AI-generated versus professional and citizen-produced news

Article Title: Explaining the AI–journalist credibility gap: generalized and source-specific judgments of news credibility

Article References: Barnidge, M., & Nah, S. (2026). Explaining the AI–journalist credibility gap: generalized and source-specific judgments of news credibility. AI &amp; SOCIETY. https://doi.org/10.1007/s00146-026-03374-2

Image Credits: AI Generated

DOI: 10.1007/s00146-026-03374-2

Keywords: AI-generated news, news credibility, journalism, bifactor modeling, trust in journalists, automated journalism, citizen journalism, machine heuristic, institutional authority, media trust, survey research, AI & Society