Movements opposing genetically modified organisms (GMOs) remain one of the most impactful protest movements in recent times, successfully suppressing the widespread global acceptance of GMOs through strategically crafted anti-GMO rhetoric. Yet, inadequate research has focused on the arguments used by GMO-promoting advocates. In this media content analysis study, inspired by the Neo-Aristotelian Method of Rhetorical Criticism (NAMRC), we analyze news articles about GMO technologies gathered from the most-read news portals in Ghana. We identify the rhetorical strategies used by GMO-promoting institutions that are reported in media interactions when the legitimacy of these technologies is questioned. We found that pro-GMO rhetoric focuses on themes of problem-solving technology, defensive advocacy, hope for the future, and scientific evidence to persuade publics. In the media coverage we analyzed, pro-GMO advocates defended both the safety of the technology and the legitimacy of scientific research and agricultural innovation. To ensure that advocacy for genetically modified crops is both responsible and credible, advocates of GMO technologies must strike a balance between conveying enthusiasm for these technologies and exercising caution about their limitations.

The COVID-19 pandemic exposed media professionals to the complex challenge of communicating scientific uncertainty. Using an automated, dictionary-based approach, we examined how different types of publications addressed scientific uncertainty at both the onset and the declared end of the pandemic. In the early stages of this health crisis, both general interest and science-focused media showed increased scientific uncertainty scores, with specialised outlets using scientific uncertainty markers more frequently. When the pandemic was declared over, science-focused publications maintained high scientific uncertainty levels across all stories, while general interest media reverted to pre-COVID-19 levels. The findings provide insights for journalists and science communicators.

Despite the rising popularity of video-based platforms, systematic guidelines for developing effective video-based science communication remain scarce. Training scientists in these skills is vital for combating misinformation and engaging audiences. This study reviewed evidence-based strategies for communicating science via video-based social media platforms, identifying 28 articles that included original video-based data and were published in the past decade. Articles were identified through library database searches, journal archives, and publication lists from relevant researchers. Predominantly focusing on YouTube (42.9%) and TikTok (28.6%), qualitative findings revealed best practices related to narrative structure, emotion and connection, video features, professionalism and quality, and social media strategies. Highlighting actionable strategies, this research provides valuable insights for scientists navigating the dynamic landscape of video-based science communication.

This paper studies how artificial intelligence was set to the agenda in the press and social media in France. By simultaneously analysing the framing of AI and the key actors who dominated the discourse on this technology in the national press and on the X and Facebook platforms, the study highlights, on the one hand, the influence of digital companies and government narratives, and on the other, the presence of alternative stakeholder perspectives that diverge from dominant discourses and contribute to political polarisation on AI-related issues such as facial recognition. Our study sheds light on how AI framing can highlight dominant and alternative narratives and visions and may contribute to the consolidation of socio-technical imaginaries in the French public sphere.

The advent of generative Artificial Intelligence (genAI) is expected to have a significant impact on journalism. In this study, we address whether this development could help mitigate the crisis in science journalism. We conducted semi-structured interviews with 30 German science journalists, asking them about the potential impact genAI may have on the news-making process (i.e., selection, production, and distribution). The results suggest that interviewees anticipate many future benefits associated with genAI, some believe that the technology is unlikely to worsen the crisis in science journalism, while others express concerns about potential negative consequences (e.g., job loss).

Based on an ethnography of the development and production of science YouTube videos – a collaboration between a German public broadcaster and social science scholars – we identify three intermediary steps through which recommendation algorithms shape science content on social media. We argue that algorithms induce changes to science content through the power they exert over the content's visibility on social media platforms. Change is driven by how practitioners interpret algorithms, infer content strategies to enhance visibility, and adjust content creation practices accordingly. By unpacking these intermediate steps, we reveal the nuanced mechanisms by which algorithms indirectly shape science content.

We assessed ChatGPT's ability to identify and categorize actors in German news media articles into societal groups. Through three experiments, we evaluated various models and prompting strategies. In experiment 1, we found that providing ChatGPT with codebooks designed for manual content analysis was insufficient. However, combining Named Entity Recognition with an optimized prompt for actor Classification (NERC pipeline) yielded acceptable results. In experiment 2, we compared the performance of gpt-3.5-turbo, gpt-4o, and gpt-4-turbo, with the latter performing best, though challenges remained in classifying nuanced actor categories. In experiment 3, we demonstrated that repeating the classification with the same model produced highly reliable results, even across different release versions.

Most public audiences in Germany receive scientific information via a variety of (digital) media; in these contexts, media act as intermediaries of trust in science by providing information that present reasons for public audiences to place their trust in science. To describe this process, the study introduces the term “trust cues”. To identify such content-related trust cues, an explorative qualitative content analysis has been applied to German journalistic, populist, social, and other (non-journalistic) online media (“n” = 158). In total, “n” = 1,329 trust cues were coded. The findings emphasize the diversity of mediated trust, with trust cues being connected to dimensions of trust in science (established: expertise, integrity, benevolence; recently introduced: transparency, dialogue). Through this analysis, the study aims for a better understanding of mediated trust in science. Deriving this finding is crucial since public trust in science is important for individual and collective informed decision-making and crises management.

This comprehensive compilation of a wide variety of science communication scholars investigating science and health journalism, brought together by editors Kim Walsh-Childers and Merryn McKinnon, leaves one with mixed impressions.

Publisher's note: a Letter by Merryn McKinnon and Kim Walsh-Childers has been published on September 5th 2025 and is available here