Relevance and Impact of Tabbed Browsing Behavior on Web Usage Mining

Viermetz, Maximilian; Stolz, Carsten; Gedov, Vassil; Skubacz, Michal

doi:10.1109/wi.2006.147

Cited by 22 publications

(12 citation statements)

References 7 publications

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“…One way to consider the referrals when modeling user navigation are referrer trees, as proposed in [16,23], where nodes represent pages and the links connect pages to their referrals. In Figure 1(d) we have two referrer trees, one starting from page 1 and the other from page 4, as both pages lack a referral.…”

Section: Definitionmentioning

confidence: 99%

Online multitasking and user engagement

Lehmann

Lalmas

Dupret

et al. 2013

Proceedings of the 22nd ACM International Conference on Conference on Information &Amp; Knowledge Management - CIKM '13

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Users often access and re-access more than one site during an online session, effectively engaging in multitasking. In this paper, we study the effect of online multitasking on two widely used engagement metrics designed to capture users browsing behavior with a site. Our study is based on browsing data of 2.5M users across 760 sites encompassing diverse types of services such as social media, news and mail. To account for multitasking we need to redefine how user sessions are represented and we need to adapt the metrics under study. We introduce a new representation of user sessions: tree-streams -as opposed to the commonly used click-streams -present a more accurate picture of the browsing behavior of a user that includes how users switch between sites (e.g., hyperlinking, teleporting, backpaging). We then discuss a number of insights on multitasking patterns, and show how these help to better understand how users engage with sites. Finally, we define metrics that characterize multitasking during online sessions and show how they provide additional insights to standard engagement metrics.

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Section: Definitionmentioning

confidence: 99%

Online multitasking and user engagement

Lehmann

Lalmas

Dupret

et al. 2013

Proceedings of the 22nd ACM International Conference on Conference on Information &Amp; Knowledge Management - CIKM '13

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“…Viermetz et al [17] showed how tabbed browsing affects the validity of search log studies. However, they analyze server side logs which do not directly contain information about tabs or windows since they only record incoming network traffic.…”

Section: Related Workmentioning

confidence: 99%

Parallel browsing behavior on the web

Huang

White

2010

Proceedings of the 21st ACM Conference on Hypertext and Hypermedia

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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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“…Parallel browsing can be only estimated when using this kind of tracking. In [4], authors introduced a model for both linear browsing (action of following a link) and parallel browsing (actions of opening a tab and switching between open tabs) and subsequently used the server-side data for modeling the user behavior. Only linear browsing is accurately observed from server data and only an estimate of possible clicktrees and paths through them can be made for parallel browsing, giving only information on how the users could be browsing in parallel, giving many possibilities.…”

Section: Parralel Web Browsing Studiesmentioning

confidence: 99%

Modeling parallel web browsing behavior for web-based educational systems

Lábaj

Bieliková

2012

2012 IEEE 10th International Conference on Emerging eLearning Technologies and Applications (ICETA)

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Abstract-When learners access web-based educational systems, they often take advantage of what current web browsers offer: multiple tabs with different pages. Whether they are solving an exercise or learning a whole new topic, they can concurrently browse other relevant objects in the educational system or even resources outside the system, on the "wild" Web. This behavior goes unnoticed or it is only estimated in the traditional web usage mining, where it is supposed that the user opens only one page at a time. However, if we could record the parallel browsing behavior, we could better understand the user's activity, goals and improve learner model employed for personalization. The paths through resources (including spawning new tabs and switching between them) of many learners also express relevance between these resources and this can be leveraged in applications such as recommendation to other learners browsing similar learning objects. In this paper, we introduce a model for parallel browsing behavior based on events tracked with client-side scripting. We realized the proposed model in the ALEF educational system, which we use in several courses and evaluate learner behavior in the system.

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Relevance and Impact of Tabbed Browsing Behavior on Web Usage Mining

Cited by 22 publications

References 7 publications

Online multitasking and user engagement

Online multitasking and user engagement

Parallel browsing behavior on the web

Modeling parallel web browsing behavior for web-based educational systems

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