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research-article

Social annotations in web search

Authors:

Aditi Muralidharan,

Zoltan Gyongyi,

Ed ChiAuthors Info & Claims

CHI '12: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

Pages 1085 - 1094

https://doi.org/10.1145/2207676.2208554

Published: 05 May 2012 Publication History

Abstract

We ask how to best present social annotations on search results, and attempt to find an answer through mixed-method eye-tracking and interview experiments. Current practice is anchored on the assumption that faces and names draw attention; the same presentation format is used independently of the social connection strength and the search query topic. The key findings of our experiments indicate room for improvement. First, only certain social contacts are useful sources of information, depending on the search topic. Second, faces lose their well-documented power to draw attention when rendered small as part of a social search result annotation. Third, and perhaps most surprisingly, social annotations go largely unnoticed by users in general due to selective, structured visual parsing behaviors specific to search result pages. We conclude by recommending improvements to the design and content of social annotations to make them more noticeable and useful.

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Cited By

Shaikh ANambiar HGhate KBanik SSen SGhosh SRaychoudhury VGanguly NSaha S(2024)Self-SLAM: A Self-supervised Learning Based Annotation Method to Reduce Labeling OverheadMachine Learning and Knowledge Discovery in Databases. Applied Data Science Track10.1007/978-3-031-70378-2_8(123-140)Online publication date: 22-Aug-2024
https://doi.org/10.1007/978-3-031-70378-2_8
Vuong TDas PRuotsalo T(2024)Associating cognitive abilities with naturalistic search behaviorJournal of the Association for Information Science and Technology10.1002/asi.24963Online publication date: 6-Nov-2024
https://doi.org/10.1002/asi.24963
Fang J(2023)Supporting Online Learning and Teaching with Social AnnotationsCompanion Proceedings of the 2023 ACM International Conference on Supporting Group Work10.1145/3565967.3571751(42-44)Online publication date: 8-Jan-2023
https://dl.acm.org/doi/10.1145/3565967.3571751
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Index Terms

Social annotations in web search
1. Human-centered computing
  1. Human computer interaction (HCI)

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Published In

cover image ACM Conferences

CHI '12: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

May 2012

3276 pages

ISBN:9781450310154

DOI:10.1145/2207676

General Chair:
Joseph A. Konstan
University of Minnesota
,
Program Chairs:
Ed H. Chi
Google
,
Kristina Höök
Mobile Life at KTH

Copyright © 2012 ACM.

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: 05 May 2012

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May 5 - 10, 2012

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Cited By

Shaikh ANambiar HGhate KBanik SSen SGhosh SRaychoudhury VGanguly NSaha S(2024)Self-SLAM: A Self-supervised Learning Based Annotation Method to Reduce Labeling OverheadMachine Learning and Knowledge Discovery in Databases. Applied Data Science Track10.1007/978-3-031-70378-2_8(123-140)Online publication date: 22-Aug-2024
https://doi.org/10.1007/978-3-031-70378-2_8
Vuong TDas PRuotsalo T(2024)Associating cognitive abilities with naturalistic search behaviorJournal of the Association for Information Science and Technology10.1002/asi.24963Online publication date: 6-Nov-2024
https://doi.org/10.1002/asi.24963
Fang J(2023)Supporting Online Learning and Teaching with Social AnnotationsCompanion Proceedings of the 2023 ACM International Conference on Supporting Group Work10.1145/3565967.3571751(42-44)Online publication date: 8-Jan-2023
https://dl.acm.org/doi/10.1145/3565967.3571751
Fang JWang YYang CLiu CWang H(2022)Understanding the Effects of Structured Note-taking Systems for Video-based Learners in Individual and Social Learning ContextsProceedings of the ACM on Human-Computer Interaction10.1145/34928406:GROUP(1-21)Online publication date: 14-Jan-2022
https://dl.acm.org/doi/10.1145/3492840
Chatterjee SChakma AGangopadhyay ARoy NMitra BChakraborty STruong KHeylen DCzerwinski MBerthouze NChetouani MNakano M(2020)LASO: Exploiting Locomotive and Acoustic Signatures over the Edge to Annotate IMU Data for Human Activity RecognitionProceedings of the 2020 International Conference on Multimodal Interaction10.1145/3382507.3418826(333-342)Online publication date: 21-Oct-2020
https://dl.acm.org/doi/10.1145/3382507.3418826
Lewandowski DKammerer Y(2020)Factors influencing viewing behaviour on search engine results pages: a review of eye-tracking researchBehaviour & Information Technology10.1080/0144929X.2020.176145040:14(1485-1515)Online publication date: 22-May-2020
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Gunaratne JZalmanson LNov O(2018)The Persuasive Power of Algorithmic and Crowdsourced AdviceJournal of Management Information Systems10.1080/07421222.2018.152353435:4(1092-1120)Online publication date: 17-Dec-2018
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Jin HWang HGong CLiu L(2018)A study on the influencing factors of consumer information-seeking behavior in the context of ambient intelligenceJournal of Ambient Intelligence and Humanized Computing10.1007/s12652-018-1005-yOnline publication date: 1-Sep-2018
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Brusilovsky PSmyth BShapira B(2018)Social SearchSocial Information Access10.1007/978-3-319-90092-6_7(213-276)Online publication date: 3-May-2018
https://doi.org/10.1007/978-3-319-90092-6_7
Farzan RBrusilovsky P(2018)Social NavigationSocial Information Access10.1007/978-3-319-90092-6_5(142-180)Online publication date: 3-May-2018
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