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Revealing the Role of User Moods in Struggling Search Tasks

Published: 18 July 2019 Publication History
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  • Abstract

    User-centered approaches have been extensively studied and used in the area of struggling search. Related research has targeted key aspects of users such as user satisfaction or frustration, and search success or failure, using a variety of experimental methods including laboratory user studies, in-situ explicit feedback from searchers and by using crowdsourcing. Such studies are valuable in advancing the understanding of search difficulty from a user's perspective, and yield insights that can directly improve search systems and their evaluation. However, little is known about how user moods influence their interactions with a search system or their perception of struggling. In this work, we show that a user's own mood. can systematically bias the user's perception, and experience while interacting with a search system and trying to satisfy an information need. People who are in activated-(un)pleasant moods tend to issue more queries than people in deactivated or neutral moods. Those in an unpleasant mood perceive a higher level of difficulty. Our insights extend the current understanding of struggling search tasks and have important implications on the design and evaluation of search systems supporting such tasks.

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    Cited By

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    • (2024)"Are we all in the same boat?" Customizable and Evolving Avatars to Improve Worker Engagement and Foster a Sense of Community in Online Crowd WorkProceedings of the CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642429(1-26)Online publication date: 11-May-2024
    • (2023)Early detection of depression using a conversational AI bot: A non-clinical trialPLOS ONE10.1371/journal.pone.027974318:2(e0279743)Online publication date: 3-Feb-2023
    • (2023)Into the Unknown: Exploration of Search Engines’ Responses to Users with Depression and AnxietyACM Transactions on the Web10.1145/358028317:4(1-29)Online publication date: 11-Jul-2023
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    1. Revealing the Role of User Moods in Struggling Search Tasks

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        cover image ACM Conferences
        SIGIR'19: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval
        July 2019
        1512 pages
        ISBN:9781450361729
        DOI:10.1145/3331184
        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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        Published: 18 July 2019

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        Author Tags

        1. information retrieval
        2. mood
        3. struggling search
        4. users

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        SIGIR'19 Paper Acceptance Rate 84 of 426 submissions, 20%;
        Overall Acceptance Rate 792 of 3,983 submissions, 20%

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        Cited By

        View all
        • (2024)"Are we all in the same boat?" Customizable and Evolving Avatars to Improve Worker Engagement and Foster a Sense of Community in Online Crowd WorkProceedings of the CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642429(1-26)Online publication date: 11-May-2024
        • (2023)Early detection of depression using a conversational AI bot: A non-clinical trialPLOS ONE10.1371/journal.pone.027974318:2(e0279743)Online publication date: 3-Feb-2023
        • (2023)Into the Unknown: Exploration of Search Engines’ Responses to Users with Depression and AnxietyACM Transactions on the Web10.1145/358028317:4(1-29)Online publication date: 11-Jul-2023
        • (2023)In a Hurry: How Time Constraints and the Presentation of Web Search Results Affect User Behaviour and ExperienceWeb Engineering10.1007/978-3-031-34444-2_16(221-235)Online publication date: 16-Jun-2023
        • (2022)Great Chain of Agents: The Role of Metaphorical Representation of Agents in Conversational CrowdsourcingProceedings of the 2022 CHI Conference on Human Factors in Computing Systems10.1145/3491102.3517653(1-22)Online publication date: 29-Apr-2022
        • (2021)On the State of Reporting in Crowdsourcing Experiments and a Checklist to Aid Current PracticesProceedings of the ACM on Human-Computer Interaction10.1145/34795315:CSCW2(1-34)Online publication date: 18-Oct-2021
        • (2021)Improving Reactions to Rejection in Crowdsourcing Through Self-ReflectionProceedings of the 13th ACM Web Science Conference 202110.1145/3447535.3462482(74-83)Online publication date: 21-Jun-2021
        • (2021)A day at the racesApplied Intelligence10.1007/s10489-021-02719-2Online publication date: 17-Aug-2021
        • (2020)Inside Out: Exploring the Emotional Side of Search Engines in the ClassroomProceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization10.1145/3340631.3394847(136-144)Online publication date: 7-Jul-2020
        • (2020)Just the Right Mood for HIT!Web Engineering10.1007/978-3-030-50578-3_26(381-396)Online publication date: 10-Jun-2020

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