While it is often assumed that searching for information to evaluate misinformation will help identify false claims, recent work suggests that search behaviours can instead reinforce belief in misleading news, particularly when users generate queries using vocabulary from the source articles. Our research explores how different query generation strategies affect news verification and whether the way people search influences the accuracy of their information evaluation. A mixed-methods approach was used, consisting of three parts: (1) an analysis of existing data to understand how search behaviour influences trust in fake news (2) a simulation of query generation strategies using a Large Language Model (LLM) to assess the impact of different query formulations on search result quality, and (3) a user study to examine how 'Boost' interventions in interface design can guide users to adopt more effective query strategies. The results show that search behaviour significantly affects trust in news, with successful searches involving multiple queries and yielding higher-quality results. Queries inspired by different parts of a news article produced search results of varying quality, and weak initial queries improved when reformulated using full SERP information. Although 'Boost' interventions had limited impact, the study suggests that interface design encouraging users to thoroughly review search results can enhance query formulation. This study highlights the importance of query strategies in evaluating news and proposes that interface design can play a key role in promoting more effective search practices, serving as one component of a broader set of interventions to combat misinformation.

Conversational agents struggle to answer questions during complex tasks such as do-it-yourself (DIY) projects and cooking due to difficulties in understanding task context and user information needs. This study examines the efficacy of integrating conversational and task context in query and document representations to enhance question answering (QA) performance in cooking tasks. We evaluated three document representations with increasing granularity on two task-based QA datasets with a total sample size of 6217 question–answer pairs: full recipe documents (document-based), segmented recipes by cooking steps (step-based), and detailed task structures (task-based). The results show step- and task-based representations outperform traditional document-based approaches by 10% on average ( ). Task-based representations provide superior performance for fact-based needs (e.g., ingredients, time, equipment) in most cases, while step-based representations better address competence needs (e.g., preparation, cooking techniques). Simple conversational history prepending of two to three turns yielded the best performance, improving results by up to 24% over no context. These results emphasise the importance of selecting a representation that matches the structure of the surrounding task in order to enhance QA performance.

Conversational agents have become increasingly integrated into our daily lives, including assisting with cooking-related tasks. To address these issues and supplement other datasets, we introduce QookA—a unique dataset featuring spoken queries, associated information needs, and answers rooted in cooking recipes. QookA overcomes shortcomings in existing datasets, laying the foundation for more effective conversational agents tailored to cooking tasks. This paper outlines the dataset construction process, analyzes the data, and explores research applications, providing a valuable resource to enhance conversational agents in the cooking domain.

With the emergence of voice assistants and large language models, conversational interaction with information has become part of everyday life. The eighth edition of the search-oriented conversational AI (SCAI) workshop brings together practitioners and researchers from various disciplines to discuss challenges and advances in conversational search systems. This year’s edition focuses on evaluations beyond relevance and accuracy and looks at conversational search from the user’s perspective. The workshop features a shared task on user-centered evaluation datasets and metrics, challenging participants to develop new and innovative ways to evaluate conversational search systems while accounting for the needs and preferences of users.

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.

Online retail has become a popular alternative to in-store shopping. However, unlike in traditional stores, users of online shops need to find the right product on their own without support from expert salespersons. Conversational search could provide a means to compensate for the shortcomings of traditional product search engines. To establish design guidelines for such virtual product search assistants, we studied conversations in a user study (N = 24) where experts supported users in finding the right product for their needs. We annotated the conversations concerning their content and conversational structure and identified recurring conversational strategies. Our findings show that experts actively elicit the users’ information needs using funneling techniques. They also use dialogue-structuring elements and frequently confirm having understood what the client was saying by using discourse markers, e.g., “mhm”. With this work, we contribute insights and design implications for conversational product search assistants.

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.