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Parallel browsing behavior on the web

Published: 13 June 2010 Publication History

Abstract

Parallel browsing describes a behavior where users visit Web pages in multiple concurrent threads. Web browsers explicitly support this by providing tabs. Although parallel browsing is more prevalent than linear browsing online, little is known about how users perform this activity. We study the use of parallel browsing through a log-based study of millions of Web users and present findings on their behavior. We identify a power law distribution in browser metrics comprising "outclicks" and tab switches, which signify the degree of parallel browsing. We find that users switch tabs at least 57.4% of the time, but user activity, measured in pageviews, is split among tabs rather than increasing overall activity. Finally, analysis of a subset of the logs focused on Web search shows that while the majority of users do not branch from search engine result pages, the degree of branching is higher for non-navigational queries. Our findings have design implications for Web sites and browsers, search interfaces, and log analysis.

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cover image ACM Conferences
HT '10: Proceedings of the 21st ACM conference on Hypertext and hypermedia
June 2010
328 pages
ISBN:9781450300414
DOI:10.1145/1810617
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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Publication History

Published: 13 June 2010

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Author Tags

  1. log mining
  2. parallel browsing
  3. tabs

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HT '10
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HT '10: 21st ACM Conference on Hypertext and Hypermedia
June 13 - 16, 2010
Ontario, Toronto, Canada

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Overall Acceptance Rate 378 of 1,158 submissions, 33%

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  • (2023)A Large-Scale Characterization of How Readers Browse WikipediaACM Transactions on the Web10.1145/358031817:2(1-22)Online publication date: 3-Apr-2023
  • (2023)When Browsing Gets Cluttered: Exploring and Modeling Interactions of Browsing Clutter, Browsing Habits, and CopingProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3580690(1-29)Online publication date: 19-Apr-2023
  • (2023)Snoopy: A Webpage Fingerprinting Framework With Finite Query Model for Mass-SurveillanceIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2022.322246220:5(3734-3752)Online publication date: 1-Sep-2023
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  • (2022)A Longitudinal Study to Understand University Students' Searching as a Learning ProcessProceedings of the Association for Information Science and Technology10.1002/pra2.64259:1(404-409)Online publication date: 14-Oct-2022
  • (2021)How Do Home Computer Users Browse the Web?ACM Transactions on the Web10.1145/347334316:1(1-27)Online publication date: 28-Sep-2021
  • (2021)Tabs.do: Task-Centric Browser Tab ManagementThe 34th Annual ACM Symposium on User Interface Software and Technology10.1145/3472749.3474777(663-676)Online publication date: 10-Oct-2021
  • (2021)Web Augmentation as a Technique to Diminish User Interactions in Repetitive TasksIEEE Access10.1109/ACCESS.2021.31041879(112686-112704)Online publication date: 2021
  • (2021)Directing and Combining Multiple Queries for Exploratory Search by Visual Interactive Intent ModelingHuman-Computer Interaction – INTERACT 202110.1007/978-3-030-85613-7_34(514-535)Online publication date: 26-Aug-2021
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