Search (9 results, page 1 of 1)

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Abstract

Purpose People face barriers and failures in various kinds of information seeking experiences. These are often attributed to either the information seeker or the system/service they use. The purpose of this paper is to investigate how and why individuals fail to fulfill their information needs in all contexts and situations. It addresses the limitations of existing studies in examining the context of the task and information seeker's strategy and seeks to gain a holistic understanding of information seeking barriers and failures. Design/methodology/approach The primary method used for this investigation is a qualitative survey, in which 63 participants provided 208 real life examples of failures in information seeking. After analyzing the survey data, ten semi-structured interviews with another group of participants were conducted to further examine the survey findings. Data were analyzed using various theoretical frameworks of tasks, strategies, and barriers. Findings A careful examination of aspects of tasks, barriers, and strategies identified from the examples revealed that a wide range of external and internal factors caused people's failures. These factors were also caused or affected by multiple aspects of information seekers' tasks and strategies. People's information needs were often too contextual and specific to be fulfilled by the information retrieved. Other barriers, such as time constraint and institutional restrictions, also intensified the problem. Originality/value This paper highlights the importance of considering the information seeking episodes in which individuals fail to fulfill their needs in a holistic approach by analyzing their tasks, information needs, strategies, and obstacles. The modified theoretical frameworks and the coding methods used could also be instrumental for future research.

Date

20. 1.2015 18:30:22

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Abstract

This volume summarizes the author's work on social information seeking (SIS), and at the same time serves as an introduction to the topic. Sometimes also referred to as social search or social information retrieval, this is a relatively new area of study concerned with the seeking and acquiring of information from social spaces on the Internet. It involves studying situations, motivations, and methods involved in seeking and sharing of information in participatory online social sites, such as Yahoo! Answers, WikiAnswers, and Twitter, as well as building systems for supporting such activities. The first part of the book introduces various foundational concepts, including information seeking, social media, and social networking. As such it provides the necessary basis to then discuss how those aspects could intertwine in different ways to create methods, tools, and opportunities for supporting and leveraging SIS. Next, Part II discusses the social dimension and primarily examines the online question-answering activity. Part III then emphasizes the collaborative aspect of information seeking, and examines what happens when social and collaborative dimensions are considered together. Lastly, Part IV provides a synthesis by consolidating methods, systems, and evaluation techniques related to social and collaborative information seeking. The book is completed by a list of challenges and opportunities for both theoretical and practical SIS work. The book is intended mainly for researchers and graduate students looking for an introduction to this new field, as well as developers and system designers interested in building interactive information retrieval systems or social/community-driven interfaces.

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Abstract

Implicit detection of relevance has been approached by many during the last decade. From the use of individual measures to the use of multiple features from different sources (multimodality), studies have shown the feasibility to automatically detect whether a document is relevant. Despite promising results, it is not clear yet to what extent multimodality constitutes an effective approach compared to unimodality. In this article, we hypothesize that it is possible to build unimodal models capable of outperforming multimodal models in the detection of perceived relevance. To test this hypothesis, we conducted three experiments to compare unimodal and multimodal classification models built using a combination of 24 features. Our classification experiments showed that a univariate unimodal model based on the left-click feature supports our hypothesis. On the other hand, our prediction experiment suggests that multimodality slightly improves early classification compared to the best unimodal models. Based on our results, we argue that the feasibility for practical applications of state-of-the-art multimodal approaches may be strongly constrained by technology, cultural, ethical, and legal aspects, in which case unimodality may offer a better alternative today for supporting relevance detection in interactive information retrieval systems.

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Abstract

The notions that information seeking is not always a solitary activity and that people working in collaboration for information intensive tasks should be studied and supported have become more prevalent in recent years. Several new research questions, methodologies, and systems have emerged around these notions that may prove to be useful beyond the field of collaborative information seeking (CIS), with relevance to the broader area of information seeking and behavior. This article provides an overview of such key research work from a variety of domains, including library and information science, computer-supported cooperative work, human-computer interaction, and information retrieval. It starts with explanations of collaboration and how CIS fits in different contexts, emphasizing the interactive, intentional, and mutually beneficial nature of CIS activities. Relations to similar and related fields such as collaborative information retrieval, collaborative information behavior, and collaborative filtering are also clarified. Next, the article presents a synthesis of various frameworks and models that exist in the field today, along with a new synthesis of 12 different dimensions of group activities. A discussion on issues and approaches relating to evaluating various parameters in CIS follows. Finally, a list of known issues and challenges is presented to provide an overview of research opportunities in this field.

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Abstract

In explicit collaborative search, two or more individuals coordinate their efforts toward a shared goal. Every day, Internet users with similar information needs have the potential to collaborate. However, online search is typically performed in solitude. Existing search systems do not promote explicit collaborations, and collaboration opportunities (collabportunities) are missed. In this article, we describe a method to evaluate the feasibility of transforming these collabportunities into recommendations for explicit collaboration. We developed a technique called pseudocollaboration to evaluate the benefits and costs of collabportunities through simulations. We evaluate the performance of our method using three data sets: (a) data from single users' search sessions, (b) data with collaborative search sessions between pairs of searchers, and (c) logs from a large-scale search engine with search sessions of thousands of searchers. Our results establish when and how collabportunities would significantly help or hinder the search process versus searches conducted individually. The method that we describe has implications for the design and implementation of recommendation systems for explicit collaboration. It also connects system-mediated and user-mediated collaborative search, whereby the system evaluates the likely benefits of collaborating for a search task and helps searchers make more informed decisions on initiating and executing such a collaboration.

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Abstract

Analyzing and modeling users' online search behaviors when conducting exploratory search tasks could be instrumental in discovering search behavior patterns that can then be leveraged to assist users in reaching their search task goals. We propose a framework for evaluating exploratory search based on implicit features and user search action sequences extracted from the transactional log data to model different aspects of exploratory search namely uncertainty, creativity, exploration, and knowledge discovery. We show the effectiveness of the proposed framework by demonstrating how it can be used to understand and evaluate user search performance and thereby make meaningful recommendations to improve the overall search performance of users. We used data collected from a user study consisting of 18 users conducting an exploratory search task for two sessions with two different topics in the experimental analysis. With this analysis we show that we can effectively model their behavior using implicit features to predict the user's future performance level with above 70% accuracy in most cases. Further, using simulations we demonstrate that our search process based recommendations improve the search performance of low performing users over time and validate these findings using both qualitative and quantitative approaches.

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Abstract

Online Q&A services are information sources where people identify their information need, formulate the need in natural language, and interact with one another to satisfy their needs. Even though in recent years online Q&A has considerably grown in popularity and impacted information-seeking behaviors, we still lack knowledge about what motivates people to ask a question in online Q&A environments. Yahoo! Answers and WikiAnswers were selected as the test beds in the study, and a sequential mixed method employing an Internet-based survey, a diary method, and interviews was used to investigate user motivations for asking a question in online Q&A services. Cognitive needs were found as the most significant motivation, driving people to ask a question. Yet, it was found that other motivational factors (e.g., tension free needs) also played an important role in user motivations for asking a question, depending on asker's contexts and situations. Understanding motivations for asking a question could provide a general framework of conceptualizing different contexts and situations of information needs in online Q&A. The findings have several implications not only for developing better question-answering processes in online Q&A environments, but also for gaining insights into the broader understanding of online information-seeking behaviors.

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Abstract

The Information Schools, also referred to as iSchools, have a unique approach to data science with three distinct components: human-centeredness, socially responsible, and rooted in context. In this position paper, we highlight and expand on these components and show how they are integrated in various research and educational activities related to data science that are being carried out at iSchools. We argue that the iSchool way of doing data science is not only highly relevant to the current times, but also crucial in solving problems of tomorrow. Specifically, we accentuate the issues of developing insights and solutions that are not only data-driven, but also incorporate human values, including transparency, privacy, ethics, fairness, and equity. This approach to data science has meaningful implications on how we educate the students and train the next generation of scholars and policymakers. Here, we provide some of those design decisions, rooted in evidence-based research, along with our perspective on how data science is currently situated and how it should be advanced in iSchools.

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Abstract

Today's complex, information-intensive problems often require people to work together. Mostly these tasks go far beyond simply searching together; they include information lookup, sharing, synthesis, and decision-making. In addition, they all have an end-goal that is mutually beneficial to all parties involved. Such "collaborative information seeking" (CIS) projects typically last several sessions and the participants all share an intention to contribute and benefit. Not surprisingly, these processes are highly interactive. Shah focuses on two individually well-understood notions: collaboration and information seeking, with the goal of bringing them together to show how it is a natural tendency for humans to work together on complex tasks. The first part of his book introduces the general notions of collaboration and information seeking, as well as related concepts, terminology, and frameworks; and thus provides the reader with a comprehensive treatment of the concepts underlying CIS. The second part of the book details CIS as a standalone domain. A series of frameworks, theories, and models are introduced to provide a conceptual basis for CIS. The final part describes several systems and applications of CIS, along with their broader implications on other fields such as computer-supported cooperative work (CSCW) and human-computer interaction (HCI). With this first comprehensive overview of an exciting new research field, Shah delivers to graduate students and researchers in academia and industry an encompassing description of the technologies involved, state-of-the-art results, and open challenges as well as research opportunities.