Take Me Out: Space and Place in Library Interactions

Information interactions are strongly affected by the place where they occur. Specific locations are ofen associated with searches on particular topics, and individual users perform different tasks in habituated places. A classic example of habituated space is the commuter who regularly reads the news on the train. This paper investigates these associations through four user studies that examine different uses of place in information interaction. Through this, we reveal the ways in which the location of information interactions makes them effective or ineffective. This extends our interpretation of the role of place in information interaction beyond established foci such as location-based search

User Intent Prediction in Information-seeking Conversations

Conversational assistants are being progressively adopted by the general population. However, they are not capable of handling complicated information-seeking tasks that involve multiple turns of information exchange. Due to the limited communication bandwidth in conversational search, it is important for conversational assistants to accurately detect and predict user intent in information-seeking conversations. In this paper, we investigate two aspects of user intent prediction in an information-seeking setting. First, we extract features based on the content, structural, and sentiment characteristics of a given utterance, and use classic machine learning methods to perform user intent prediction. We then conduct an in-depth feature importance analysis to identify key features in this prediction task. We find that structural features contribute most to the prediction performance. Given this finding, we construct neural classifiers to incorporate context information and achieve better performance without feature engineering. Our findings can provide insights into the important factors and effective methods of user intent prediction in information-seeking conversations.Comment: Accepted to CHIIR 201

How Sensitivity Classification Effectiveness Impacts Reviewers in Technology-Assisted Sensitivity Review

All government documents that are released to the public must first be manually reviewed to identify and protect any sensitive information, e.g. confidential information. However, the unassisted manual sensitivity review of born-digital documents is not practical due to, for example, the volume of documents that are created. Previous work has shown that sensitivity classification can be effective for predicting if a document contains sensitive information. However, since all of the released documents must be manually reviewed, it is important to know if sensitivity classification can assist sensitivity reviewers in making their sensitivity judgements. Hence, in this paper, we conduct a digital sensitivity review user study, to investigate if the accuracy of sensitivity classification effects the number of documents that a reviewer correctly judges to be sensitive or not (reviewer accuracy) and the time that it takes to sensitivity review a document (reviewing speed). Our results show that providing reviewers with sensitivity classification predictions, from a classifier that achieves 0.7 Balanced Accuracy, results in a 38% increase in mean reviewer accuracy and an increase of 72% in mean reviewing speeds, compared to when reviewers are not provided with predictions. Overall, our findings demonstrate that sensitivity classification is a viable technology for assisting with the sensitivity review of born-digital government documents

IWILDS'22 - Third International Workshop on Investigating Learning During Web Search

Since its inception, the World Wide Web has become a major information source, consulted for a diversity of informational tasks. With an abundance of information available online, Web search engines have been a main entry point, supporting users in finding suitable Web content for ever more complex information needs. The IWILDS workshop series invites research on complex search activities related to human learning. It provides an interdisciplinary platform for the presentation and discussion of recent research on human learning on the Web, welcoming perspectives from computer & information science, education and psychology

A Framework for Information Accessibility in Large Video Repositories

International audienceOnline videos are a medium of choice for young adults to access or receive information, and recent work has highlighted that it is a particularly effective medium for adults with intellectual disability, by its visual nature. Reflecting on a case study presenting fieldwork observations of how adults with intellectual disability engage with videos on the Youtube platform, we propose a framework to define and evaluate the accessibility of such large video repositories, from an informational perspective. The proposed framework nuances the concept of information accessibility from that of the accessibility of information access interfaces themselves (generally catered for under web accessibility guidelines), or that of the documents (generally covered in general accessibility guidelines). It also includes a notion of search (or browsing) accessibility, which reflects the ability to reach the document containing the information. In the context of large information repositories, this concept goes beyond how the documents are organized into how automated processes (browsing or searching) can support users. In addition to the framework we also detail specifics of document accessibility for videos. The framework suggests a multi-dimensional approach to information accessibility evaluation which includes both cognitive and sensory aspects. This framework can serve as a basis for practitioners when designing video information repositories accessible to people with intellectual disability, and extends on the information presentation guidelines such as suggested by the WCAG. Publication rights licensed to ACM. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only

On Birthing Dancing Stars: The Need for Bounded Chaos in Information Interaction

While computers causing chaos is acommon social trope, nearly the entirety of the history of computing is dedicated to generating order. Typical interactive information retrieval tasks ask computers to support the traversal and exploration of large, complex information spaces. The implicit assumption is that they are to support users in simplifying the complexity (i.e. in creating order from chaos). But for some types of task, particularly those that involve the creative application or synthesis of knowledge or the creation of new knowledge, this assumption may be incorrect. It is increasingly evident that perfect order—and the systems we create with it—support highly-structured information tasks well, but provide poor support for less-structured tasks.We need digital information environments that help create a little more chaos from order to spark creative thinking and knowledge creation. This paper argues for the need for information systems that offerwhat we term ‘bounded chaos’, and offers research directions that may support the creation of such interface

Examining and Supporting Laypeople's Learning in Online Health Information Seeking

It has long been understood that knowledge acquisition is an important component in the information seeking process [2,18]. Further, empirical studies have demonstrated that learning is a common phenomenon in information seeking [8,10,20]. However, for users, especially laypeople, who must gain knowledge through their interactions with a search engine, the current general-purpose search engine does not sufficiently support learning through search. Health information seeking (HIS, hereafter) is a domain-specific search [14], where users who possess higher knowledge tend to have better strategies and performances in solving their search tasks [3,21]. While learning clearly plays an important role in the HIS process, there has been little research in this area. Little is known about the factors that might enhance or impede such learning during onlineHIS. Therefore, this project aims at examining health consumers, especially laypeople’s search as learning behaviors and performances. A mixed method design will be adopted, consisting of experimental-based studies and interviews. So far, we have conducted 24 user studies and semi-structured interviews, investigating the source selection behaviors in the HIS tasks with increasing levels of learning goals. The results of this phase of the study will be used to guide the following analysis and predict laypeople’s knowledge levels in the HIS process and provide corresponding support