As conversational search becomes more pervasive, it becomes increasingly important to understand the users’ underlying information needs when they converse with such systems in diverse domains. We conduct an in situ study to understand information needs arising in a home cooking context as well as how they are verbally communicated to an assistant. A human experimenter plays this role in our study. Based on the transcriptions of utterances, we derive a detailed hierarchical taxonomy of diverse information needs occurring in this context, which require different levels of assistance to be solved. The taxonomy shows that needs can be communicated through different linguistic means and require different amounts of context to be understood. In a second contribution, we perform classification experiments to determine the feasibility of predicting the type of information need a user has during a dialogue using the turn provided. For this multi-label classification problem, we achieve average F1 measures of 40% using BERT-based models. We demonstrate with examples which types of needs are difficult to predict and show why, concluding that models need to include more context information in order to improve both information need classification and assistance to make such systems usable.

We present two empirical studies to investigate users’ expectations and behaviours when using digital assistants, such as Alexa and Google Home, in a kitchen context: First, a survey (N = 200) queries participants on their expectations for the kinds of information that such systems should be able to provide. While consensus exists on expecting information about cooking steps and processes, younger participants who enjoy cooking express a higher likelihood of expecting details on food history or the science of cooking. In a follow-up Wizard-of-Oz study (N = 48), users were guided through the steps of a recipe either by an active wizard that alerted participants to information it could provide or a passive wizard who only answered questions that were provided by the user. The active policy led to almost double the number of conversational utterances and 1.5 times more knowledge-related user questions compared to the passive policy. Also, it resulted in 1.7 times more knowledge communicated than the passive policy. We discuss the findings in the context of related work and reveal implications for the design and use of such assistants for cooking and other purposes such as DIY and craft tasks, as well as the lessons we learned for evaluating such systems.

Featured snippets that attempt to satisfy users’ information needs directly on top of the first search engine results page (SERP) have been shown to strongly impact users’ post-search attitudes and beliefs. In the context of debated but scientifically answerable topics, recent research has demonstrated that users tend to trust featured snippets to such an extent that they may reverse their original beliefs based on what such a snippet suggests; even when erroneous information is featured. This paper examines the effect of featured snippets in more nuanced and complicated search scenarios concerning debated topics that have no ground truth and where diverse arguments in favor and against can legitimately be made. We report on a preregistered, online user study (N = 182) investigating how the stances and logics of evaluation (i.e., underlying reasons behind stances) expressed in featured snippets influence post-task attitudes and explanations of users without strong pre-search attitudes. We found that such users tend to not only change their attitudes on debated topics (e.g., school uniforms) following whatever stance a featured snippet expresses but also incorporate the featured snippet’s logic of evaluation into their argumentation. Our findings imply that the content displayed in featured snippets may have large-scale undesired consequences for individuals, businesses, and society, and urgently call for researchers and practitioners to examine this issue further.