This project seeks to analyze the rise of Search Engine Optimization (SEO) in Indian news to understand its broader consequences in the Indian news industry. Using interviews with key stakeholders, analysis of news content and a focus on the dynamics between journalistic and SEO experts in newsrooms this presentation seeks to advance three key arguments. First it shows how SEO has changed the culture of newsrooms and their approach to news to focus a continuous mindset of churn as they focus on writing stories about trending keywords and continuously updating. Secondly, it analyzes the news content to show the rise of formulaic stories (similar across publications) that written to garner traffic (e.g. “how to” and “trending” content) instead of traditional journalistic copies. Lastly, it shows that the cohabitation of technical personnel with journalistic ones in the newsrooms creates many opportunities for conflict as well as some moments of mutual learning and course-correction.

Generative AI (GenAI) represents the third major crisis for the press, following disruptions caused by the web and social media. This article examines how GenAI’s capacity to automate content production undermines the authenticity of public communication, threatening press freedom’s role in democratic societies. Building on Ananny’s (2018) networked press freedom framework, Jungherr and Schroeder’s (2023) analysis of AI-mediated public arenas, and Lee’s (2020) authenticity model, we identify three critical challenges: the distortion of source legitimacy, inequitable access to reliable AI-generated information, and the erosion of human interaction in public discourse. Through mixed-methods analysis—including case studies of newsroom AI adoption (e.g., BuzzFeed, Wall Street Journal) and quantitative data on media decline (Pew Research, 2000–2024)—we argue that existing responses to media crises, such as regulatory bargaining codes and public subsidies, fail to address GenAI’s unique threats. Synthetic content obscures provenance, entrenches information asymmetries, and disrupts feedback-loops within the public, which are key in democratic deliberation. To counteract these risks, we propose three principles aimed at strengthening press freedom: (1) mandatory transparency for different uses of AI-generated content, (2) public AI infrastructure to ensure fair access and open research on GenAI, and (3) legal safeguards preserving human agency in editorial processes. This work contributes to cross-disciplinary debates in AI ethics, media policy, and democratic theory, offering actionable frameworks to prevent GenAI from exacerbating epistemic inequality. As a work in progress, it calls for empirical validation through stakeholder interviews, underlining the urgency of re-inserting human-centric values into an increasingly synthetic media ecosystem.

Data journalism (DJ) seeks to enhance audience comprehension and engagement by integrating statistical information, diverse sources, data visualizations, and journalistic style. However, the way DJ stories are framed—whether through an analytic approach that prioritizes precision or an affective approach that emphasizes emotional engagement—may shape audience interactivity in distinct ways. This study examines how journalistic style influences audience engagement in DJ by analyzing 6,400 New York Times (NYT) stories and 785,883 comments from 2014 to 2022.

Using computational text analysis and mediation modeling, we assess how DJ stories balance analytic and affective elements and how these stylistic choices impact user interaction. The findings indicate that DJ stories tend to adopt a more affective and less analytic style compared to traditional journalism. While affective framing increases comment volume, it is negatively associated with conversation depth. In contrast, analytic framing and static visualizations contribute to deeper discussions but attract fewer initial comments. DJ stories, overall, generate fewer comments than traditional stories, yet when comments do appear, they are more likely to develop into conversations.

These results suggest a trade-off in DJ: an affective approach fosters broader engagement, while an analytic approach and static visualizations support in-depth discussions. This study highlights the evolving role of DJ in shaping audience interactivity and underscores the need for news organizations to balance emotional resonance with analytical clarity to foster both engagement and substantive discourse.

Since 2020, the vast majority of media outlets in Europe have adopted the cloud as their infrastructure of choice. These services are, by-and-large, provided by Big Tech platforms, who also have a considerable amount of power over the broadcasting and distribution of news to online audiences. However, despite the possibility that the cloud becomes a new vector of dependency of journalism on Silicon Valley, the infrastructural transformation of the sector has received little attention. This paper addresses this question by asking why news media outlets have adopted cloud infrastructures. Building on over 50 interviews with media professionals from France and the Netherlands who had direct knowledge of their companies’ infrastructure, it shows that the cloud was consistently presented as being more reliable, cheaper and easier to scale-up than other options. Having established the reasons for the move to the cloud, the paper then considers what these discourses tell us about contemporary corporate practices concerning digital infrastructure, paying particular attention to the way in which discourses around scale, favoured by Big Tech companies, are being redeployed in other industries to justify strategic choices. Finally, the contribution closes by considering how this type of empirical research into corporate infrastructural practices in the media ecosystem can help us better understand both notions of dependence in the media sector specifically, and platform capitalism’ power logics.