Search (14 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

Source

Aslib journal of information management. 69(2017) no.4, S.441-459

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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.

RSWK

Social Media / Datenerhebung / Information Retrieval / Kooperation

Series

The information retrieval series ; vol.38

Subject

Social Media / Datenerhebung / Information Retrieval / Kooperation

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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.

Series

Advances in information science

Source

Journal of the Association for Information Science and Technology. 65(2014) no.2, S.215-236

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Abstract

Support for explicit collaboration in information-seeking activities is increasingly recognized as a desideratum for search systems. Several tools have emerged recently that help groups of people with the same information-seeking goals to work together. Many issues for these collaborative information-seeking (CIS) environments remain understudied. The authors identified awareness as one of these issues in CIS, and they presented a user study that involved 42 pairs of participants, who worked in collaboration over 2 sessions with 3 instances of the authors' CIS system for exploratory search. They showed that while having awareness of personal actions and history is important for exploratory search tasks spanning multiple sessions, support for group awareness is even more significant for effective collaboration. In addition, they showed that support for such group awareness can be provided without compromising usability or introducing additional load on the users.

Footnote

Erratum in: Journal of the American Society for Information Science and Technology, 61(2010) no.11, S.2377.

Source

Journal of the American Society for Information Science and Technology. 61(2010) no.10, S.1970-1986

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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.

Source

Journal of the Association for Information Science and Technology. 67(2016) no.5, S.1182-1197

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Abstract

Most information retrieval (IR) systems consider relevance, usefulness, and quality of information objects (documents, queries) for evaluation, prediction, and recommendation, often ignoring the underlying search process of information seeking. This may leave out opportunities for making recommendations that analyze the search process and/or recommend alternative search process instead of objects. To overcome this limitation, we investigated whether by analyzing a searcher's current processes we could forecast his likelihood of achieving a certain level of success with respect to search performance in the future. We propose a machine-learning-based method to dynamically evaluate and predict search performance several time-steps ahead at each given time point of the search process during an exploratory search task. Our prediction method uses a collection of features extracted from expression of information need and coverage of information. For testing, we used log data collected from 4 user studies that included 216 users (96 individuals and 60 pairs). Our results show 80-90% accuracy in prediction depending on the number of time-steps ahead. In effect, the work reported here provides a framework for evaluating search processes during exploratory search tasks and predicting search performance. Importantly, the proposed approach is based on user processes and is independent of any IR system.

Source

Journal of the Association for Information Science and Technology. 67(2016) no.7, S.1607-1623

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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.

Content

Inhalt: Part I Introduction.- Introduction.- Collaboration.- Collaborative Information Seeking (CIS) in Context.- Part II Conceptual Understanding of CIS.- Frameworks for CIS Research and Development.- Toward a Model for CIS.- Part III CIS Systems, Applications, and Implications.- Systems and Tools for CIS.- Evaluation.- Conclusion.- Ten Stories of Five Cs.- Brief Overview of Computer-Supported Cooperative Work (CSCW).- Brief Overview of Computer-Supported Collaborative Learning (CSCL).- Brief Overview of Computer-Mediated Communication (CMC).

Series

The Information Retrieval Series ; 34

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Abstract

Online question-answering (Q&A) services are becoming increasingly popular among information seekers. We divide them into two categories, social Q&A (SQA) and virtual reference (VR), and examine how experts (librarians) and end users (students) evaluate information within both categories. To accomplish this, we first performed an extensive literature review and compiled a list of the aspects found to contribute to a "good" answer. These aspects were divided among three high-level concepts: relevance, quality, and satisfaction. We then interviewed both experts and users, asking them first to reflect on their online Q&A experiences and then comment on our list of aspects. These interviews uncovered two main disparities. One disparity was found between users' expectations with these services and how information was actually delivered among them, and the other disparity between the perceptions of users and experts with regard to the aforementioned three characteristics of relevance, quality, and satisfaction. Using qualitative analyses of both the interviews and relevant literature, we suggest ways to create better hybrid solutions for online Q&A and to bridge the gap between experts' and users' understandings of relevance, quality, and satisfaction, as well as the perceived importance of each in contributing to a good answer.

Source

Journal of the American Society for Information Science and Technology. 63(2012) no.10, S.2020-2036

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Abstract

Communication and coordination are considered essential components of successful collaborations, and provision of awareness is a highly valuable feature of a collaborative information seeking (CIS) system. In this article, we investigate how providing different kinds of awareness support affects people's coordination behavior in a CIS task, as reflected by the communication that took place between them. We describe a laboratory study with 84 participants in 42 pairs with an experimental CIS system. These participants were brought to the laboratory for two separate sessions and given two exploratory search tasks. They were randomly assigned to one of the three systems, defined by the kind of awareness support provided. We analyzed the messages exchanged between the participants of each team by coding them for their coordination attributes. With this coding, we show how the participants employed different kinds of coordination during the study. Using qualitative and quantitative analyses, we demonstrate that the teams with no awareness, or with only personal awareness support, needed to spend more time and effort doing coordination than those with proper group awareness support. We argue that appropriate and adequate awareness support is essential for a CIS system for reducing coordination costs and keeping the collaborators well coordinated for a productive collaboration. The findings have implications for system designers as well as cognitive scientists and CIS researchers in general.

Source

Journal of the American Society for Information Science and Technology. 64(2013) no.6, S.1122-1143

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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.

Source

Journal of the Association for Information Science and Technology. 66(2015) no.9, S.1897-1912

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Abstract

Community Question-Answering (CQA) sites have become popular venues where people can ask questions, seek information, or share knowledge with a user community. Although responses on CQA sites are obviously slower than information retrieved by a search engine, one of the most frustrating aspects of CQAs occurs when an asker's posted question does not receive a reasonable answer or remains unanswered. CQA sites could improve users' experience by identifying potential answerers and routing appropriate questions to them. In this paper, we predict the potential answerers based on question content and user profiles. Our approach builds user profiles based on past activity. When a new question is posted, the proposed method computes scores between the question and all user profiles to find the potential answerers. We conduct extensive experimental evaluations on two popular CQA sites - Yahoo! Answers and Stack Overflow - to show the effectiveness of our algorithm. The results show that our technique is able to predict a small group of 1000 users from which at least one user will answer the question with a probability higher than 50% in both CQA sites. Further analysis indicates that topic interest and activity level can improve the correctness of our approach.

Source

Journal of the Association for Information Science and Technology. 69(2018) no.10, S.1246-1258

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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.

Source

Journal of the Association for Information Science and Technology. 70(2019) no.11, S.1223-1235

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Source

Information processing and management. 51(2015) no.5, S.643-661

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Source

Journal of the Association for Information Science and Technology. 68(2017) no.2, S.438-449