Generative AI (GenAI) tools are transforming information seeking, but their fluent, authoritative responses risk overreliance and discourage independent verification and reasoning. Rather than replacing the cognitive work of users, GenAI systems should be designed to support and scaffold it. Therefore, this paper introduces an LLM-based conversational copilot designed to scaffold information evaluation rather than provide answers and foster digital literacy skills. In a pre-registered, randomised controlled trial (N=261) examining three interface conditions including a chat-based copilot, our mixed-methods analysis reveals that users engaged deeply with the copilot, demonstrating metacognitive reflection. However, the copilot did not significantly improve answer correctness or search engagement, largely due to a "time-on-chat vs. exploration" trade-off and users’ bias toward positive information. Qualitative findings reveal tension between the copilot’s Socratic approach and users’ desire for efficiency. These results highlight both the promise and pitfalls of pedagogical copilots, and we outline design pathways to reconcile literacy goals with efficiency demands.

Many users struggle with effective online search and critical evaluation, especially in high-stakes domains like health, while often overestimating their digital literacy. Thus, in this demo, we present an interactive search companion that seamlessly integrates expert search strategies into existing search engine result pages. Providing context-aware tips on clarifying information needs, improving query formulation, encouraging result exploration, and mitigating biases, our companion aims to foster reflective search behaviour while minimising cognitive burden. A user study demonstrates the companion’s successful encouragement of more active and exploratory search, leading users to submit 75% more queries and view roughly twice as many results, as well as performance gains in difficult tasks. This demo illustrates how lightweight, contextual guidance can enhance search literacy and empower users through micro-learning opportunities. While the vision involves real-time LLM adaptivity, this study utilises a controlled implementation to test the underlying intervention strategies.

Legal retrieval is a widely studied area in Information Retrieval (IR) and a key task in this domain is retrieving relevant cases based on a given query case, often done by applying language models as encoders to model case similarity. Recently, Tang et al. proposed CaseLink, a novel graph-based method for legal case retrieval, which models both cases and legal charges as nodes in a network, with edges representing relationships such as references and shared semantics. This approach offers a new perspective on the task by capturing higher-order relationships of cases going beyond the stand-alone level of documents. However, while this shift in approaching legal case retrieval is a promising direction in an understudied area of graph-based legal IR, challenges in reproducing novel results have recently been highlighted, with multiple studies reporting difficulties in reproducing previous findings. Thus, in this work we reproduce CaseLink, a graph-based legal case retrieval method, to support future research in this area of IR. In particular, we aim to assess its reliability and generalizability by (i) first reproducing the original study setup and (ii) applying the approach to an additional dataset. We then build upon the original implementations by (iii) evaluating the approach's performance when using a more sophisticated graph data representation and (iv) using an open large language model (LLM) in the pipeline to address limitations that are known to result from using closed models accessed via an API. Our findings aim to improve the understanding of graph-based approaches in legal IR and contribute to improving reproducibility in the field. To achieve this, we share all our implementations and experimental artifacts with the community.

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.

Loss of privacy and encounters with misinformation are two challenges individuals are likely to encounter in their search for information on the web. These challenges have potential negative impacts, especially in search domains such as health search. Existing information retrieval (IR) systems offer users little (if any) guidance as to how to reduce the likelihood of such negative impacts. The sum of these problems provides motivation for experiments to identify elements of existing IR environments that might provide low-cost options to the user for improved search outcomes.

Chatbots that can automatically answer customer requests have become a common feature on e-commerce Web sites. There are many factors that might affect overall customer satisfaction with such services. We explore how adding language style to e-commerce chatbots can be used to increase user satisfaction, perceived product value, user interest in a product, and user engagement with a chatbot service. We conducted an experimental pilot study, where two chatbots were used to sell theatre tickets: one communicating in modern English and one in a Shakespearean-style dialect. 169 participants interacted with a randomly-assigned version of the chatbot. The results indicate that the bot talking in modern English showed a significantly higher user satisfaction, whereas the Shakespearean-styled chatbot showed higher user engagement and perceived product value. It was also found that the modern chatbot version was more often referred to as being 'easy to use', whereas the Shakespearean chatbot version was more often referred to as being 'fun to use'.

From their impacts to potential threats, privacy and misinformation are a recurring top news story. Social media platforms (e.g. Facebook) and information retrieval (IR) systems (e.g. Google), are now in the public spotlight to address these issues. Our research investigates an approach, known as Nudging, applied to the domain of IR, as a potential means to minimize impacts and threats surrounding both matters. We perform our study in the space of health search for two reasons. First, encounters with misinformation in this space have potentially grave outcomes. Second, there are many potential threats to personal privacy as a result of the data collected during a search task. Adopting methods and a corpus from previous work as the foundation, our study asked users to determine the effectiveness of a treatment for 10 medical conditions. Users performed the tasks on 4 variants of a search engine results page (SERP) and a control, with 3 of the SERP's being a Nudge (re-ranking, filtering and a visual cue) intended to reduce impacts to privacy with minimal impact to search result quality. The aim of our work is to determine the Nudge that is least impactful to good decision making while simultaneously increasing privacy protection. We find privacy impacts are significantly reduced for the re-ranking and filtering strategies, with no significant impacts on quality of decision making.

Within traditional games design, incorporating progressive difficulty is considered of fundamental importance. But despite the widespread intuition that progression could have clear benefits in Games-With-A-Purpose (GWAPs)–e.g., for training non-expert annotators to produce more complex judgements– progression is not in fact a prominent feature of GWAPs; and there is even less evidence on its effects. In this work we present an approach to progression in GWAPs that generalizes to different annotation tasks with minimal, if any, dependency on gold annotated data. Using this method we observe a statistically significant increase in accuracy over randomly showing items to annotators.

Privacy concerns are becoming a dominant focus in search applications, thus there is a growing need to understand implications of efforts to address these concerns. Our research investigates a search system with privacy warning labels, an approach inspired by decision making research on food nutrition labels. This approach is designed to alert users to potential privacy threats in their search for information as one possible avenue to address privacy concerns. Our primary goal is to understand the extent to which attitudes towards privacy are linked to behaviors that protect privacy. In the present study, participants were given a set of fact-based decision tasks from the domain of health search. Participants were rotated through variations of search engine results pages (SERPs) including a SERP with a privacy warning light system. Lastly, participants completed a survey to capture attitudes towards privacy, behaviors to protect privacy, and other demographic information. In addition to the comparison of interactive search behaviors of a privacy warning SERP with a control SERP, we compared self-report privacy measures with interactive search behaviors. Participants reported strong concerns around privacy of health information while simultaneously placing high importance on the correctness of this information. Analysis of our interactive experiment and self-report privacy measures indicate that 1) choice of privacy-protective browsers has a significant link to privacy attitudes and privacy-protective behaviors in a SERP and 2) there are no significant links between reported concerns towards privacy and recorded behavior in an information retrieval system with warnings that enable users to protect their privacy.

Professional search is a problem area in which many facets of information retrieval are addressed, both system-related (e.g. distributed search) and user-related (e.g. complex information needs), and the interface between user and system (e.g. supporting exploratory search tasks). Professional search tasks have specific requirements, different from the requirements of generic web search. The aim of this workshop is to bring together researchers to work on the requirements and challenges of professional search from different angles. We will have an interactive workshop where researchers not only present their scientific results but also work together on the definition of future challenges and solutions with input from information professionals. The workshop will deliver a roadmap of research directions for the years to come.

Recent research has shown the usefulness of using collective user interaction data (e.g., query logs) to recommend query modification suggestions for Intranet search. However, most of the query suggestion approaches for Intranet search follow an ``one size fits all'' strategy, whereby different users who submit an identical query would get the same query suggestion list. This is problematic, as even with the same query, different users may have different topics of interest, which may change over time in response to the user's interaction with the system.

Stronger engagement and greater participation is often crucial to reach a goal or to solve an issue. Issues like the emerging employee engagement crisis, insufficient knowledge sharing, and chronic procrastination. In many cases we need and search for tools to beat procrastination or to change people's habits. Gamification is the approach to learn from often fun, creative and engaging games. In principle, it is about understanding games and applying game design elements in a non-gaming environments. This offers possibilities for wide area improvements. For example more accurate work, better retention rates and more cost effective solutions by relating motivations for participating as more intrinsic than conventional methods. In the context of Information Retrieval (IR) it is not hard to imagine that many tasks could benefit from gamification techniques. Besides several manual annotation tasks of data sets for IR research, user participation is important in order to gather implicit or even explicit feedback to feed the algorithms. Gamification, however, comes with its own challenges and its adoption in IR is still in its infancy. Given the enormous response to the first and second GamifIR workshops that were both co-located with ECIR, and the broad range of topics discussed, we now organized the third workshop at SIGIR 2016 to address a range of emerging challenges and opportunities.

Information systems that utilise contextual information have the potential of helping a user identify relevant information more quickly and more accurately than systems that work the same for all users and contexts. Contextual information comes in a variety of types, often derived from records of past interactions between a user and the information system. It can be individual or group based. We are focusing on the latter, harnessing the search behaviour of cohorts of users, turning it into a domain model that can then be used to assist other users of the same cohort. More specifically, we aim to explore how such a domain model is best utilised for profile-biased summarisation of documents in a navigation scenario in which such summaries can be displayed as hover text as a user moves the mouse over a link. The main motivation is to help a user find relevant documents more quickly. Given the fact that the Web in general has been studied extensively already, we focus our attention on Web sites and similar document collections. Such collections can be notoriously difficult to search or explore. The process of acquiring the domain model is not a research interest here; we simply adopt a biologically inspired method that resembles the idea of ant colony optimisation. This has been shown to work well in a variety of application areas. The model can be built in a continuous learning cycle that exploits search patterns as recorded in typical query log files. Our research explores different summarisation techniques, some of which use the domain model and some that do not. We perform task-based evaluations of these different techniques—thus of the impact of the domain model and profile-biased summarisation—in the context of Web site navigation.

Modern search engines have been moving away from simplistic interfaces that aimed at satisfying a user's need with a single‐shot query. Interactive features are now integral parts of web search engines. However, generating good query modification suggestions remains a challenging issue. Query log analysis is one of the major strands of work in this direction. Although much research has been performed on query logs collected on the web as a whole, query log analysis to enhance search on smaller and more focused collections has attracted less attention, despite its increasing practical importance. In this article, we report on a systematic study of different query modification methods applied to a substantial query log collected on a local website that already uses an interactive search engine. We conducted experiments in which we asked users to assess the relevance of potential query modification suggestions that have been constructed using a range of log analysis methods and different baseline approaches. The experimental results demonstrate the usefulness of log analysis to extract query modification suggestions. Furthermore, our experiments demonstrate that a more fine‐grained approach than grouping search requests into sessions allows for extraction of better refinement terms from query log files.

A concept hierarchy created from a document collection can be used for query recommendation on Intranets by ranking terms according to the strength of their links to the query within the hierarchy. A major limitation is that this model produces the same recommendations for identical queries and rebuilding it from scratch periodically can be extremely inefficient due to the high computational costs. We propose to adapt the model by incorporating query refinements from search logs. Our intuition is that the concept hierarchy built from the collection and the search logs provide complementary conceptual views on the same search domain, and their integration should continually improve the effectiveness of recommended terms. Two adaptation approaches using query logs with and without click information are compared. We evaluate the concept hierarchy models (static and adapted versions) built from the Intranet collections of two academic institutions and compare them with a state-of-the-art log-based query recommender, the Query Flow Graph, built from the same logs. Our adaptive model significantly outperforms its static version and the query flow graph when tested over a period of time on data (documents and search logs) from two institutions' Intranets.

Natural language processing (NLP) is becoming much more robust and applicable in realistic applications. One area in which NLP has still not been fully exploited is information retrieval (IR). In particular we are interested in search over intranets and other local Web sites. We see dialogue-driven search which is based on a largely automated knowledge extraction process as one of the next big steps. Instead of replying with a set of documents for a user query the system would allow the user to navigate through the extracted knowledge base by making use of a simple dialogue manager. Here we support this idea with a first task-based evaluation that we conducted on a university intranet. We automatically extracted entities like person names, organizations and locations as well as relations between entities and added visual graphs to the search results whenever a user query could be mapped into this knowledge base. We found that users are willing to interact and use those visual interfaces. We also found that users preferred such a system that guides a user through the result set over a baseline approach. The results represent an important first step towards full NLP-driven intranet search.

While much research has been performed on query logs collected for major Web search engines, query log analysis to enhance search on smaller and more focused collections has attracted less attention. Our hypothesis is that an intranet search engine can be enhanced by adapting the search system to real users' search behaviour through exploiting its query logs. In this work we describe how a constantly adapting domain model can be used to identify and capture changes in intranet users' search requirements over time. We employ an algorithm that dynamically builds a domain model from query modifications taken from an intranet query log and employs a decay measure, as used in Machine Learning and Optimisation methods, to promote more recent terms. This model is used to suggest query refinements and additions to users and to elevate seasonally relevant terms. A user evaluation using models constructed from a substantial university intranet query log is provided. Statistical evidence demonstrates the system's ability to suggest seasonally relevant terms over three different academic trimesters. We conclude that log files of an intranet search engine are a rich resource to build adaptive domain models, and in our experiments these models significantly outperform sensible baselines.

Abstract The Web provides a massive knowledge source, as do intranets and other electronic document collections. However, much of that knowledge is encoded implicitly and cannot be applied directly without processing into some more appropriate structures. Searching, browsing, question answering, for example, could all benefit from domain‐specific knowledge contained in the documents, and in applications such as simple search we do not actually need very “deep” knowledge structures such as ontologies, but we can get a long way with a model of the domain that consists of term hierarchies. We combine domain knowledge automatically acquired by exploiting the documents' markup structure with knowledge extracted on the fly to assist a user with ad hoc search requests. Such a search system can suggest query modification options derived from the actual data and thus guide a user through the space of documents. This article gives a detailed account of a task‐based evaluation that compares a search system that uses the outlined domain knowledge with a standard search system. We found that users do use the query modification suggestions proposed by the system. The main conclusion we can draw from this evaluation, however, is that users prefer a system that can suggest query modifications over a standard search engine, which simply presents a ranked list of documents. Most interestingly, we observe this user preference despite the fact that the baseline system even performs slightly better under certain criteria.

No abstract available.