Web search engines have evolved drastically over the past two decades, transitioning from simple link providers to direct answer providers. AI technologies, particularly generative large language models, have accelerated this shift by embedding conversational and personalized features directly into search systems. As a result, user expectations and approaches to search have fundamentally changed.

Web search engines have long been the primary gateway to online information, but generative AI chatbots are emerging as alternative tools. Yet we lack empirical understanding of how users choose between these tools and how their information-seeking behaviors differ across these contexts. Understanding how users select, use, and assess information retrieval (IR) systems is essential for evaluating them in ways that reflect user expectations across contexts. To shed light on user perceptions and usage of IR systems, we surveyed 84 participants about their recent search engine and GenAI chatbot use, analyzing task types, contextual triggers, outcomes, and tool preferences. Our findings reveal that users approach search engines and GenAI chatbots with distinct philosophies. With search engines, they follow a 'Browse and Verify' approach, prioritizing reliability and source verifiability. With GenAI chatbots, they adopt a 'Delegate and Consume' approach, valuing quick summarization and personalized content. Despite these stated preferences, actual usage shows that users frequently turn to GenAI chatbots for actionable guidance in high-stakes domains such as health and relationship matters, hinting at a divergence between stated philosophies and real-world behavior. These findings underscore the need for IR systems that support reliable information assessment, transparent sources, and user-guided evaluation.

Internet users highly rely on and trust web search engines, such as Google, to find relevant information online. However, scholars have documented numerous biases and inaccuracies in search outputs. To improve the quality of search results, search engines employ various content moderation practices such as interface elements informing users about potentially dangerous websites and algorithmic mechanisms for downgrading or removing low-quality search results. While the reliance of the public on web search engines and their use of moderation practices is well-established, user attitudes towards these practices have not yet been explored in detail. To address this gap, we first conducted an overview of content moderation practices used by search engines, and then surveyed a representative sample of the US adult population (N=398) to examine the levels of support for different moderation practices applied to potentially misleading and/or potentially offensive content in web search. We also analyzed the relationship between user characteristics and their support for specific moderation practices. We find that the most supported practice is informing users about potentially misleading or offensive content, and the least supported one is the complete removal of search results. More conservative users and users with lower levels of trust in web search results are more likely to be against content moderation in web search.

TikTok currently is the fastest growing social media platform with over 1 billion active monthly users of which the majority is from generation Z. Arguably, its most important success driver is its recommendation system. Despite the importance of TikTok’s algorithm to the platform’s success and content distribution, little work has been done on the empirical analysis of the algorithm. Our work lays the foundation to fill this research gap. Using a sock-puppet audit methodology with a custom algorithm developed by us, we tested and analysed the effect of the language and location used to access TikTok, follow- and like-feature, as well as how the recommended content changes as a user watches certain posts longer than others. We provide evidence that all the tested factors influence the content recommended to TikTok users. Further, we identified that the follow-feature has the strongest influence, followed by the like-feature and video view rate. We also discuss the implications of our findings in the context of the formation of filter bubbles on TikTok and the proliferation of problematic content.