Artificial Intelligence

The first session at the ACSPRI 2024 conference is on generative AI, and starts with Lachlan Watson. He is interested in the use of AI assistance to analyse public policy submissions, here in the context of Animal Welfare Victoria’s draft cat management strategy. Feedback could be in the form of written submissions, surveys, or both, and needed to be analysed using quantitative approaches given the substantial volume of submission.

The organisation chose Relevance AI as a tool for this – this is a low code AI solution not unlike ChatGPT, but data is hosted in a private environment and none …

And the final speaker in this final AoIR 2024 conference session is the excellent Fabio Giglietto, whose focus is on coding Italian news data using Large Language Models. This worked with some 85,000 news articles shared on Facebook during the 2018 and 2022 Italian elections, and first classified such URLs as political or non-political; it then produced and clustered text embeddings for these articles, and used GPT-4-turbo to classify the dominant topics in these clusters.

This required considerable prompt crafting, especially also to ensure that prompts remained within the LLM’s token limits. Key challenges here included the choice of LLM …

The next speaker in this final AoIR 2024 conference session is the great Hendrik Meyer, whose interest is in detecting stances in climate change coverage. This focusses especially on climate change debates in German news media, focussing on climate protests, discussions about speed limits, and discussions about heating and heat pump regulations.

Here stances might be better understood as evaluations related to a given issue or policy, and Large Language Models can be useful tools in assessing this, but this also requires considerable prompt crafting in order to generate consistent results. Computational costs for doing so (especially with complex prompts) …

The next speaker in this session at the AoIR 2024 conference is my QUT colleague Tariq Choucair, whose focus is especially on the use of LLMs in stance detection in news content. A stance is a public act by a social actors, achieved dialogically through communication, which evaluates objects, positions the self and other subjects, and aligns with other subjects within a sociocultural field.

Here, the focus is broadly on stances towards issues, persons, groups, and organisations. There are some tools for doing so, but they mainly focus on English-language content, are designed for specific types of data, and tend …

The second speaker in this final session at the AoIR 2024 conference is Bruna Silveira de Oliveira, whose focus is on using LLMs to study content in the Brazilian manosphere. Extremist groups in this space seek legitimisation, and the question here is whether LLMs can be used productively to analyse their posts.

This analysis focusses on some 2,500 episodes of Brazilian masculinist podcasts across ten streaming platforms. It engaged in an assisted content analysis using OpenAI’s GPT-4 model, and explored whether this could identify detailed variables in the content. The podcast episodes were transcribed using automated tools, and 52 episodes …

The final (!) session at this wonderful AoIR 2024 conference is on content analysis, and starts with Ahrabhi Kathirgamalingam. Her interest is especially on questions of agreement and disagreement between content codings; the gold standard here has for a long time been intercoder reliability, but this tends to presume a single ground truth which may not exist in all coding contexts.

The concept of ‘constructs of marginalisation’ might be useful here: marginalised people are underrepresented; existing structural power defines who defines such constructs; they are historically and culturally shaped; and explicit as well as ambiguous and evasive language that discriminates …

The final presenters in this session at the AoIR 2024 conference are Yarden Skop and Anna Schjøtt Hansen; their interests are in the third-party fact-checking network employed by Meta. This operates on the basis of a Meta-provided online dashboard that highlights potentially problematic content, and the dashboard’s operation directs fact-checking away from political content spread by major political figures, and towards other forms of content.

Many fact-checking organisations around the world now substantially rely on income from Meta through their engagement in its fact-checking programme; this is part of a global post-publication debunking turn, but also creates a dependency on …

The next speaker in this AoIR 2024 conference session is Simran Agarwal, whose interest is in platformisation intermediaries in the Indian news industry. Her interest here is especially in the meso-layer of intermediaries, where AI-driven machine learning tools provide strategic counsel to newsrooms, broker interactions between platforms and publishers with the aim to ‘help’, ‘assist’, or ‘free’ journalists, and appear as certified partners.

Such intermediaries may be understood as cultural intermediaries, algorithmic experts, metricians, or content recommendation platforms; they may complement platforms or assist content production, and AI systems in particular retool, reshape, and rationalise the news. To explore this …

The next speaker in this AoIR 2024 conference session is Nanna Bonde Thylstrup, who begins by noting the critical role of data annotation practices in shaping the machine learning process underlying generative AI; such annotation is a world-making practice, must align with editorial values and the journalistic ethos of objectivity, and can of course also reproduce pre-existing societal biases.

In a sense, then, the algorithms of generative AI must also seek to reproduce (and perhaps improve upon) the famous ‘gut feeling’ of conventional human journalism. The present project worked with developers and data annotations at Danish news organisations – but …

The next speaker in this AoIR 2024 conference session is Nadja Schaetz, whose interest is in AI hype in news coverage. Journalism has often uncritically covered the rise of generative AI, and swallowed the claims of AI companies about the capacities of their tools; this project collaborated with the Associated Press Local AI Initiative and conducted participant observation in local newsrooms to understand journalistic reactions to this initiative. Through the project AP worked with five newsrooms to provide AI-supported technologies.

What the study observed was not AI hype as such, however: not simply a gap between expectations and reality of …