This study examines the limitations of traditional search intent frameworks in the evolving landscape of digital information retrieval. We argue that Broder's (2002) widely used taxonomy of informational, navigational, and transactional intent fails to capture contemporary search behaviors due to two major shifts: first, traditional search engines have evolved from simple hyperlink providers into platforms offering rich snippets and direct answers; second, large language model (LLM) chatbots have emerged as alternative information retrieval tools. Through an online survey we conducted (N=82) where participants reflected on their actual search histories across both platforms (246 sessions each), we identified three key shortcomings of Broder’s (2002) taxonomy: emergent patterns outside existing categories, dissolution of boundaries between intent types, and statistically uneven distribution of categories across platforms.

Based on our analysis of participants' reflections on their search histories and the identified shortcomings of traditional search intent taxonomies, we propose a novel user-centered framework. This framework shifts the focus from what users search for to why they search and how they use information. Our model has the following dimensions: immediate search goals (knowledge-oriented, solution-oriented, or resource-oriented), contextual triggers, outcome realizations, and overarching purposes connected to fundamental human values. This approach, grounded in Xie's (2002) model of interactive information retrieval and Schwartz's Theory of Basic Values (2012), provides a more comprehensive understanding of search behavior by considering the entire search journey rather than isolated queries, offering valuable insights for designing more responsive information retrieval systems.

This study investigates whether a shared cultural experience—what we term the "TikTok Baseline"—exists among Australian TikTok users. While existing research suggests social media platforms’ recommendation systems contribute to homogenizing users' cultural experiences, TikTok's highly personalized, responsive algorithmic system remains understudied.

To map the "TikTok Baseline", we employed a methodological approach that minimized personal data exposure and interaction with content. Data collection occurred four times daily over a three-month period (May-July 2024) from multiple Australian locations, resulting in metadata from 5,100 unique videos from TikTok’s generic For You Page (FYP). We developed and validated an AI-driven video analysis tool using Google Gemini's 2.0 Flash multimodal model to enhance traditional metadata analysis.

This paper reports on the preliminary phase of a comprehensive study of Australian experiences of algorithmic culture on TikTok. At the heart of the project is a comprehensive data donation-based study of how Australian content creators and users experience TikTok’s recommender system. This study makes three key contributions. First, we establish whether the concept of a reasonably stable TikTok Baseline manifests in real-world data, secondly, we examine the fundamental characteristics of such a baseline, and thirdly we suggest a rigorous computational methodology for examining TikTok baseline in the hope that our approach can be replicated in other territories and contexts. By addressing challenges in algorithmic observability and AI-driven content analysis, our findings offer critical implications for platform governance and regulatory efforts. This research advances our understanding of algorithmic culture, demonstrating how TikTok's recommender system both personalizes and standardizes user experiences.

This study aims at understanding the feelings, perceptions and beliefs of climate activists aged between 17 and 25, and how these relate to their social media use. We investigate the platforms young climate activists use to receive climate change-related information, the types of information they encounter, and the emotions such information evokes in them through 20 in-depth interviews. Our findings reveal that Instagram is their most important source of climate change-related content, and that the negative valence of the content on the app occasionally leads them to delete the app when the content becomes overwhelming. Moreover, the interviewees explained that the feelings they felt from online content depended on its type and on whom it was posted by, suggesting that the valence of the content encountered in the media influences may affect whether one will feel eco-anxiety or not. Given this link, the participants mentioned that it is important for them to be emotionally ready to see climate change-related content. This understanding led many of them to curate the content they see online – for example, by choosing not to follow or engage with certain accounts and types of content on social media – therefore highlighting their agency, and the acts that online climate-change related content can elicit. Altogether, these findings show an important connection between online climate-change related content and eco-anxiety, while highlighting the role of participants’ agency – which opens up potential new avenues for research at the intersection of online information exposure and eco-anxiety.

Search engines are one of the most common ways for users to discover news and political information. As a result, the content and sources prioritized by these platforms are of paramount importance. Previous research has shown that search engines can provide biased outputs by, for example, discriminating against certain social groups or favoring certain viewpoints. However, there has been a gap in understanding how users search for information using these systems and, in particular, how they formulate their search queries. Yet the choice of query is crucial, as it largely determines the information users are exposed to, and thus may lead to particular biases.

To address this gap, we conducted a representative survey of Swiss citizens (N = 1,100) in which we investigate how voters search for information about an environmental popular initiative that was voted on in Switzerland in February 2025. In particular, we test the assumption that selective exposure (or the tendency to prefer information that confirms one's own beliefs) is more pronounced when users are presented with a limited number of options (e.g., search engine autocomplete suggestions) than when they formulate them independently. We also examine how pre-existing knowledge about the topic, beliefs about climate change, political attitudes, and cognitive factors affect query formulation.

This study contributes to existing research by demonstrating how individual characteristics of users can influence information seeking behavior and how selective exposure can manifest itself under different search conditions.