how highlighting change affects people’s web interactions
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How Highlighting Change Affects People’s Web Interactions. Jaime Teevan, Susan Dumais & Dan Liebling Microsoft Research. Web Dynamics. Content Changes. JanuaryFebruaryMarch April May JuneJuly August September. - PowerPoint PPT PresentationTRANSCRIPT
How Highlighting Change Affects People’s Web Interactions
Jaime Teevan, Susan Dumais & Dan LieblingMicrosoft Research
Web Dynamics
January February March April May June July August September
Content Changes
• Studies of content change [Adar et al., Fetterly et al.]
• Web doubles and half the pages change yearly• Frequency and degree of change characterized
Web Dynamics
January February March April May June July August September
Content Changes
People Revisit
January February March April May June July August September
• People revisit Web pages frequently– Half of visits are revisits [Adar et al., Tauscher & Greenberg]
– A third of searches are for re-finding [Teevan et al.]
• Revisitation relates to change– 66% of revisits are to changed pages [Adar et al.]
– 20% of the content changes [Adar et al.]
– Often motivated by change [Adar et al., Keller et al.]
– Change can cause problems [Obendorf et al., Teevan et al.]
Web Dynamics
January February March April May June July August September
Content Changes
People Revisit
January February March April May June July August September
Today’s Browse and Search Experiences
Ignores …
Systems That Expose Web Change
• Historical access to Web pages– Internet Archives (archive.org)
• Subscription to Web content change– RSS, Web slices– Monitoring support [Kellar et al.]
• In-situ awareness of Web content change– symbols– Dynamo, Difference Engine, WebCQ
new
Always on
In-situ
New to you
Non-intrusive
DiffIE
Changes to page since your last visit
April 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
Content Changes
People Revisit
April 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
SURVEYHow often do pages change?o o o o o
How often do you revisit?o o o o o
SURVEYHow often do pages change?o o o o o
How often do you revisit?o o o o o
Studying DiffIE30 people
install DiffIE
People Revisit More
• Perception of revisitation remains constant– How often do you revisit? – How often are revisits to view new content?
• Actual revisitation increases– First week: 39.4% of visits are revisits– Last week: 45.0% of visits are revisits
• Why do people revisit more?
14%
Revisited Pages Change More
• Perception of change increases– What proportion of pages change regularly?– How often do you notice unexpected change?
• Amount of change seen increases– First week: 21.5% revisits changed by 6.2%– Last week: 32.4% revisits changed by 9.5%
• Exposed change drives visits to changed pages
51+%
17%
8%
Perceptions of Change Reinforced
• Change by page type• Pages that change a lot change more• Pages that change a little change less
News pagesMessage boards, forums, news groupsSearch engine resultsBlogs you readPages with product informationWikipedia pagesCompany homepagesPersonal home pages of people you knowReference pages (dictionaries, yellow pages, maps)
Change little
Change a lot
• People revisit Web pages more• The pages revisited change more• Perceptions of change are reinforced
Affects of Highlighting Change
Change Adar, Teevan, Dumais & Elsas. The Web changes everything: Understanding the dynamics of Web Content. WSDM ’09 (Best Student Paper).Elsas & Dumais. Leveraging temporal dynamics of document content in relevance ranking. WSDM ’10.
Revisitation Adar, Teevan & Dumais. Large scale analysis of Web revisitation patterns. CHI ’08 (Best Paper).Teevan, Adar, Jones & Potts. Information re-retrieval: Repeat queries in Yahoo’s logs. SIGIR ’07.Tyler & Teevan. Large scale query log analysis of re-finding. WSDM ’10.
Relationship Adar, Teevan & Dumais. Resonance on the Web: Web dynamics and revisitation patterns. CHI ’09.
DiffIETeevan, Dumais, Liebling & Hughes. Changing how people view changes on the Web. UIST ’09.Teevan, Dumais & Liebling. A longitudinal study of how highlighting Web content change affects people’s Web interactions. CHI ’10 (Best Paper).
Thank you.Jaime Teevan
http://research.microsoft.com/~teevan