Jean Burgess’s research while affiliated with Queensland University of Technology and other places

This paper charts the emergence of a distinct category of research-dedicated GenAI platforms, which we term Research GenAI or RGAI. These platforms are explicitly marketed to a cross-disciplinary academic audience, promising to automate research discovery and writing tasks, such as identifying/summarising published research, writing literature reviews, conducting data analysis, and synthesising findings. RGAI platforms (e.g., Consensus, Elicit, Research Rabbit, Scholarcy, Scite, SciSpace) are rapidly being adopted, in a context of experimentation, uncertainty, and controversy. We define the contours of Research GenAI by mapping the history and development of RGAI platforms and developing a preliminary typology of RGAI. We situate RGAI platforms within the scholarly economy and ongoing processes of platformisation and automation of academic work. We make a case for the need to understand RGAI platforms as complex sociotechnical systems that intersect with social, ethical, institutional, and legal questions, and demonstrate this approach through an STS-informed walkthrough of two notable RGAI platforms: Consensus and Elicit. In this presentation we present our findings generated from these walkthroughs and explore the implications of the technologies for the academic publishing industry.

In an academic milieu in which a lot of critical attention is dedicated to the data-grabbing, algorithmically biased, and asymmetrical power of massive techno-corporations, this panel explores how a focus on situated ordinary practices can provide us with a more complex, nuanced, and even at times contradictory account of what happens when pervasive digital technologies are experienced in everyday life. It does so via a combination of empirical research and theoretical development in a number of platformised and datafied domains: social media, music and generative AI. Juxtaposition of these topics and approaches, and dialogue between the authors and audience, will, we hope, forefront struggles, disputes, and ambivalences concerning what people actually do with the digital systems that they engage with. As feminist scholars established many years ago, the everyday is trivial and yet profoundly politically charged; ordinary experience is always imbued with power dynamics, hierarchies, asymmetries, and emerging modes of governmentality. Using empirically-based case studies, the panel engages with a tricky question: how can we theorise power and agency while making sense of the textures and poetics of everyday life, if the everyday is precisely in the unremarkable, the unnoticed, in that which escapes our grasp?

Political polarisation – at the level of individual issues or broader ideologies, and expressed through differences of opinion on policies, divergent affective attachments, contrary interpretations of available information, or distinct patterns of interaction with other partisans – is necessarily always closely related to the information that individuals and groups engage with as they form and reinforce their own opinions about a given issue or topic, and contest the views of others. News in all its forms, from legacy to emerging, and mainstream to fringe media, continues to play a particularly important role in such information diets, but news coverage can itself be polarised and polarising. This panel addresses polarisation in and by the news through a series of papers that examine the various stages of the news production and engagement process. Drawing on innovative methods and novel datasets, these five papers offer new perspectives on the patterns and dynamics that affect news quality, news distribution, news engagement, and news fact-checking in digital and social media environments. In combination, they offer a new and comprehensive overview of how we might further investigate news polarisation in contemporary contexts. Paper 1 assesses polarisation in news coverage. Centred on the issue of climate change, it investigates patterns of news coverage across the media landscape in Australia – a country which has been particularly severely affected by extreme climate events in recent years. The paper highlights the challenges in accessing full-text news content at scale, and utilises a novel combination of manual and computational content coding techniques to investigate the patterns of news polarisation across four dimensions. Paper 2 investigates what sources of news are frequently recommended to users of prominent search engine Google News. Drawing on a long-term data donation project in Australia, the paper reviews the range of sources recommended for a variety of political and controversial search queries, and assesses the breadth of the political spectrum that these prominent recommendations represent. It also examines whether such patterns differ across individual queries or broader topic categories. Paper 3 shifts our attention to the sharing of news content on social media platforms. Drawing on long-term, large-scale datasets from Facebook and Twitter, it analyses the sharing of links to Australian news sources during 2022, and thereby reveals patterns of interactional and interpretive polarisation. These may be related to the political alignment of users and outlets, but also to news quality and other factors. Paper 4 takes a network approach to the study of news sharing on Twitter in Germany. Assessing the topical content of links to German news shared during one month in 2023, and the political affinities of the users sharing these links, the study finds marked differences between the sharing practices and patterns of left-leaning, conservative, and far-right users, as well as between sharing practices on different topics. Finally, Paper 5 closes the loop by examining the role perceptions of political fact-checkers, and their potential to contribute polarisation or depolarisation. Drawing on a series of interviews with staff in Australian fact-checking organisations, it provides a deep insight into their self-understanding, especially with respect to the extent and limitations of their impact on polarised debates in society. In combination, then, these five papers address questions of news polarisation throughout the stages of the journalistic process from news production to distribution and engagement all the way to the critical scrutiny of political statements reported in the news. Overall, they make substantial new conceptual, methodological, and empirical contributions to the study of news polarisation. Extended abstracts for all five papers are included in the submission.

Against the backdrop of calls for greater platform transparency, this exploratory article investigates Meta’s ‘Why Am I Seeing This Ad’ (WAIST) feature, which is positioned as a consumer-level explanation of Meta’s advertising model. Drawing on our own walkthroughs of Facebook and Instagram and data from the Australian Ad Observatory, we find the feature falls short in two ways. First, the explanations do not always align with how the system and its audience-building tools are sold to and used by advertisers. Second, the feature is focused narrowly on single ads and individual users, doing nothing to generate understanding of the patterns and sequences of targeted advertising in relation to other users or over time. We propose both platform practices and independent research strategies that could help to fill this gap between individual explanations, population-level patterns of targeted online advertising and the societal issues associated with it.

The #nostalgiacores are a series of interrelated hashtags on Instagram and TikTok where users recirculate content from the digital and consumer cultures of the 1990s and 2000s – childhood play centres, dead malls, long-gone toys, and superseded game consoles and phones. In this article, we explore these digital cultures using a critical platform studies approach that involves a combination of network analysis and close textual analysis augmented with purpose-built machine vision tools. We scrape a collection of 359,150 images from Instagram that used one or more of 30 ‘-cores’ hashtags (such as #y2kcore, #webcore and #childhoodcore) that we chose following a period of immersive qualitative investigation of #nostalgiacore scenes on Instagram during 2021 and 2022. 10,000 Instagram images were then randomly selected and processed using a purpose-built unsupervised machine vision model that clusters images together based on their similarities. This research is part of a multi-year project where we develop hybrid digital methods for critically simulating and exploring the interplay between our image-making practices and the algorithmic systems that cluster and curate them. By combining computational approaches with critical platform and cultural studies approaches we speculatively explore both practices of curation and their interplay with the algorithmic classification and recommendation models of digital platforms. Our platform-oriented mode of textual analysis helps us to explore how our digital cultures are both symbolically and technically nostalgic. Instagram users in the #nostalgiacore scene recirculate images from the past as part of practices of critically reflecting on digital platforms and consumer cultures. At the same time those images are recuperated as archives used to train the algorithmic models that optimise attention on digital media platforms like Instagram.

‘Diversity’ is a heavily freighted and multivalent keyword in the global digital media environment. The recommender systems used by platforms are particularly acute sites of development and debate around the political, cultural and technical issues ‘diversity’ signifies. Drawing on a review of computer science publications on recommender systems in media and entertainment as well as a survey of recent advances in media and cultural policy scholarship, this short article performs a pragmatic close reading of diversity in these intersecting fields. We note that attention must be paid to the specific challenges and politics of diversity not only in particular cultural fields but also in local cultural contexts, drawing on examples from music and SVOD platforms to flesh out these questions and the practical possibilities that arise from them.

This article draws on a qualitative interview-based study and the framework of the ‘critical incident’ to explore whether, how and for whom the first year of the COVID-19 pandemic saw an increased uptake of data-driven automation in Australian newsrooms and with what implications for the field. Our findings show that, while news workers combined and adapted existing technologies to meet increased demands for rolling, data-driven coverage of the pandemic, structural and institutional factors prevented the uptake and embedding of forms of data journalism and editorial automation that may have assisted with providing more timely and effective public health information. The findings highlight the importance of COVID-19 as both an acute event and an ongoing situation that has revealed and prompted reflection on the practical and political challenges of data flows between government agencies and news organisations.

For more than a decade, digital advertising has been the primary means of funding online content and services. The evolution of digital advertising towards algorithmically targeted advertising, believed to be highly personalized and tailored to the individual, has presented new challenges for public oversight. Whereas previously, public concern centred on the content of ads and their exposure to audiences, the rise of platform-based advertising means focus has shifted to the distribution of ads and how they reach us. In response to public concerns and regulatory pressures, companies such as Meta (the parent of Facebook) have introduced transparency tools for researchers and consumers to ‘explain’ the function of advertising on the platform, including the Ad Library and the “Why Am I Seeing This Ad” feature. Despite being a central feature of Meta’s response towards increasing external scrutiny, little is known about how the WAIST feature works, or how it operates at a population level. In response we offer a description of WAIST data collected at scale, informed from a nationwide citizen data donation project of Facebook advertising. We analyse this data with a view to better understand Meta’s algorithmic advertising system, and to inform questions regarding the sufficiency of WAIST as an algorithmic explanatory mechanism for users.