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 marks the third systemic crisis confronting journalism in the 21st century. Building on crisis framings from journalism studies, we trace how technological disruption has sequentially destabilized news packaging (web, 2000s), news distribution (social media, 2010s), and now content production itself (GenAI, early-2020s). This latest disruption threatens both newsroom sustainability and the press's democratic function of organizing public deliberation.

We identify three critical challenges GenAI poses to journalism. First, by concentrating power among actors controlling computing infrastructure and data, GenAI amplifies inequalities in content production at the network architecture level. Second, synthetic interactions undermine the interpersonal trust and dialogic practices necessary for forming publics, creating an unequal playing field between humans and GenAI-leveraging actors. Third, by introducing a production mode based on statistical plausibility rather than source verification, GenAI disrupts the chain of information custody that journalists have historically relied upon to support democratic decision-making with trusted information.

We then analyze emerging policy responses targeting each of these three specific threats: fair access initiatives designed to challenge the hold of GenAI companies over data and compute; human agency protections to ensure meaningful oversight in automated systems; and transparency-enhancing technologies to provide mechanisms of source traceability. We show how licensing negotiations, cooperative data frameworks, and technical standards for content provenance represent the emerging principles guiding newsrooms and policymakers through a process of resistance and adaptation.This article describes how established press freedom principles are challenged in the GenAI era, and identifies pathways forward amid socio-technical destabilization.

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.