Search Engines

Day two of the SEASON 2025 conference in Hamburg starts with a keynote by Anne Beaulieu, reflecting on the purpose and future of search. Search is now transforming considerably, especially given the intrusion of generative AI into the contiguous mix of uses for search, the impact of disinformation, the receding of the public sphere, and the impossibility of opting out from these systems; there is an urgent need to respond to this by rethinking search.

The way that students now engage with search engines and generative AI tools, for instance, reveals some of these changes and points to many of …

The final speaker on this first day of the SEASON 2025 conference is Kristofer Söderström, exploring differences between conventional search and the use of ChatGPT as an alternative. For users, practical distinctions between these tools may increasingly blur, considering the embedding of these technologies into each other; users may prefer LLMs because of their turn-based interrogability, though, and trust them more readily because of the convincing way in which results are presented.

How might we research and understand these practices, then? Kristofer’s study examined how Reddit users compare and contrast ChatGPT and Google Search, collecting some 271 threads from r/ChatGPT …

The second speaker in this session at the SEASON 2025 conference is Aurelie Herbelot, presenting the PeARS search engine as an experiment in building a different type of Web search. Conventional search has significant issues with truth, biases, information verification, market monopolisation, and environmental impacts; one way of addressing these problems could be to decentralise search engine platforms, in the same way that Mastodon has sought to decentralise and federate social media.

So, there are now several PeARS instances, which are each dedicated to a specific topic of interest and curate information on their specialty topic. Linked together, this then …

The final session on this first day of the SEASON 2025 conference starts with Jennifer Gnyp, whose interest is in integrating language complexity into search; this is critical for inclusive search technologies. Inclusion only works if it is not the task of individuals to adjust and adapt to the societal mainstream, but if society removes the barriers that exclude such individuals; their needs must be considered from the beginning.

Key aspects to consider here are readability and comprehensibility, but readability focusses only on formal features while comprehensibility focusses on content and meaning, and measures the ease of understanding. This requires …

The final speaker in this session at the SEASON 2025 conference is Nora Lindemann, who will focus especially on the role of Large Language Models in the information ecosystem. It’s important to note here that knowledge is embodied and situated, while information is broken-down knowledge that can be transferred through communication. This is also relational, where a relation is a connection between two entities that is constituted by their specific interaction and modulated by power.

Online information access is now rapidly changing, from (intransparently) algorithmically mediated conventional search results to the intrusion of AI-generated summaries into search results pages, or …

The next speakers in this session at the SEASON 2025 conference are Jutta Haider and Malte Rödl, whose interests are in what they call ignorance logics in search, with a particular focus on environmental issues. Algorithmic systems are involved in the shaping of knowledge, and of what is knowable and can be known; and societal responses to ecological crises, in particular, are now failing not so much because of a lack of knowledge but because of the possibility of ignorance. This is due in part also because of the curation of information by algorithmic black boxes.

Which worlds are made …

The first speaker in the first paper session at the SEASON 2025 conference is Frans van der Sluis, whose focus is on information quality in information retrieval. Judging information quality is hard: often, there is an inherent uncertainty around truth, and a question about whether a statement can be reliably justified as true. Practices such as cherry-picking, bothsidesism, and framing exploit such uncertainties. Overall, then, information quality might mean any of accuracy, comprehensiveness, expertise, usefulness, bias, and more.

Search engines tend to objectify such quality, mostly by assessing relevance; Google also seeks to includes expertise, authoritativeness, and trustworthiness factors, though …

After a week spent in Brussels and at the 25th anniversary of the Center for Internet Research in Aarhus, I’ve now arrived in Hamburg for the inaugural Search Engines and Society (SEASON) 2025 conference, which begins with a keynote by the great Matthias Spielkamp, the founder of German NGO AlgorithmWatch, who is also a partner in our ARC Centre of Excellence for Automated Decision-Making and Society. His keynote reflects on the past ten years of AlgorithmWatch’s efforts to promote algorithmic accountability.

AlgorithmWatch is a non-profit NGO based in Berlin and Zürich, seeking to ensure that algorithms serve to strengthen …

I’m presenting some early results of our large-scale dynamic practice mapping of Australian climate change discussions on Facebook later in this next session at the Weizenbaum Conference, but we begin with a paper by Konstantin Lackner, Markus Uhlmann, and Viktoria Horn. Their focus is on news navigation and recommendation: recommendations enable users to navigate information overload, but also create potential monetary gain for content sources.

Recommendations can be problematic because they optimise for retention and attention, and therefore for profit; this is also increasingly done through AI; and the result of such recommendations may be that users no longer …

The final session on day one of the Weizenbaum Conference starts with a second paper by Victoria Vziatysheva, whose focus here is on how users perceive search engine bias. Search engines are amongst the most critical elements of Internet infrastructure, but are sometimes criticised for supposed biases in their results; this may affect how users engage with them.

Users tend to trust algorithmic systems more when they are perceived to be fair, and such trust is also affected by users’ knowledge about these systems: users with limited knowledge tend to be more trusting. Search engine users tend to have folk …