analysing parallel and passive web browsing behavior and...

Analysing Parallel and Passive Web Browsing Behaviorand its Effects on Website Metrics

Christian von der WethEmail: vonderweth@nus.edu.sg

August 11, 2014

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Online Browsing Behavior

Potential benefits● Improving design and usability of websites and browsers● Assessing the popularity of websites● Advancing ranking algorithms for search engines

Emerging and rising trends affecting browsing behavior

● Passive browsing (e.g., listening to online radio while cooking)● New Web technologies (e.g., Ajax, WebSockets)● Evolving Web demographics (e.g., “Facebook Generation”)● Browsing while on the go

August 11, 2014

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Related Work

Server-side studies● Analysis of Web server or search engine transaction logs● Limited to analyzing click streams or revisitation behavior

● Insufficient granularity and detail of collected data

Client-side studies● Special browsers or ass-ons to capture browsing behavior● Typically conducted as lab studies investigating specific tasks

● Unsuitable to elicit everyday browsing behavior

August 11, 2014

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DOBBS in a Nutshell

DOBBS = DERI Online Browsing Behavior Study● Client-side approach, but unsupervised field study

Core: Browser add-on● “install-&-forget” application● Logs wide range of events● Sends events to server

Important features● Non-intrusive● Anonymous● Privacy-preserving Central DB

...new tab openednew page loadedwindow maximizeduser inactiveuser activelink clicked...

August 11, 2014

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Privacy Preservation

Applied techniques to preserve users' privacy● Complete anonymisation (user = random integer)● Encryption of all sensitive information (i.e., URL data)● User in full control the stop logging at any time● No logging of key strokes and explicit user input

We have nothing to hide● Project website with all details and dataset for download● Add-ons are open-source under very open BSD license:

http://code.google.com/p/deri-dobbs/

August 11, 2014

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Technical Limitations

Problems beyond our means to avoid● Network / connection failures● Browser errors (crashes or other bugs)● Unexpected termination (e.g., to do SIGTERM / SIGKILL)

Incomplete data unavoidable

Two basic approaches to deal with incomplete data● Filtering out affected session information● Adding estimates for missing data

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Evaluation – Focus of This Work

Parallel browsing● Usage of tabbed browsing and/or multiple browser windows● Switching between different tabs

Passive browsing● Times user a inactive / idling while browsing the Web● Two means to measure idle times

Explicit: special events fired by browser Implicit: prolonged absence of logged events

Effects of parallel and passive browsingon quantifying the popularity of websites

August 11, 2014

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Parallel Browsing: Windows vs. Tabs

Parallel browsing as common phenomenon● Tabbed browsing particularly common● Degree of parallel browsing very different across users● For this data: multiple windows XOR multiple tabs

August 11, 2014

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Parallel Browsing: Re-using Open Tabs

Main results● Most tabs used for one or very few “rounds”● Not shown: 6% of loaded pages were never visible● Large difference regarding re-using tabs for multiple page loads

avarge number of page loads per session

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Passive Browsing (1)

Session duration vs. idle time● The longer a session, the longer a user is idling● Idle time quickly dominates over active time

Passive browsing very common phenomenon

avarge number of page loads per session

August 11, 2014

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Passive Browsing (2)

Idle time as interesting metric● Different methods to quantify users' idle time applicable● Different methods describe different aspects of behavior

Important: careful selection of method and careful interpretation of results

number of sessionswithin clock hour

average session length

August 11, 2014

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Website Popularity

Main results:● Often loaded does not imply long on display

How absorbing is a website?● Long on display does not imply the user was active

How engaging is a website?

Novel notions for defining website popularity

August 11, 2014

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Reranking of Websites

New metrics to quantify the popularity of websites● Client-side phenomenons – tabbed browsing, idling, etc. –

do significantly affect “classic” rankings● Expressiveness of metric often depends on type of service

Alexa Visit time Page Loads How absorbing? How engaging?

1 Google (1) Google Facebook Facebook LinkedIn

2 Facebook (2) Facebook Google Twitter Facebook

3 YouTube (3) YouTube YouTube YouTube Twitter

4 LinkedIn (11) LinkedIn LinkedIn LinkedIn Google

5 Twitter (12) Twitter Twitter Google YouTube

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Outlook

Graph-based analysisof browsing sessions

● Nodes = page loads● Node size = loaded time● Edges = page navigation

Application of graphalgorithms

● Out/in-degrees● Shortest paths● Diameter● ...

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Conclusions

Lessons learned – apart from current results● Incomplete data unavoidable, but valid ways to deal with it● Abundance of data requires careful analysis & interpretation● Still a challenge: spreading the word● Capabilities of DOBBS go far beyond available datasets

How to participate● Install DOBBS add-on ( 20 seconds) – that's it

How to get started with the dataset● Download dataset from http://dobbs.deri.ie● Bundle includes useful scripts and example queries● ...or just contact me