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Enhancing Web Interactions Through Change Highlighting Study

Explore how highlighting Web content changes affects revisitation patterns, perception of change, and dynamics of online experiences. Identify influences on user behavior and the importance of monitoring and exposing content adjustments.

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Enhancing Web Interactions Through Change Highlighting Study

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  1. How Highlighting Change Affects People’s Web Interactions Jaime Teevan, Susan Dumais & Dan Liebling Microsoft Research

  2. Web Dynamics Content Changes January February March April May June July August September • Studies of content change [Adar et al., Fetterly et al.] • Web doubles and half the pages change yearly • Frequency and degree of change characterized

  3. January February March April May June July August September People Revisit Web Dynamics • 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.] Content Changes • 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.] January February March April May June July August September

  4. January February March April May June July August September People Revisit Today’s Browse and Search Experiences Ignores … Web Dynamics Content Changes January February March April May June July August September

  5. 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

  6. DiffIE New to you Always on Non-intrusive In-situ Changes to page since your last visit

  7. 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 People Revisit Studying DiffIE SURVEY How often do pages change? o oooo How often do you revisit? o oooo 30 people install DiffIE SURVEY How often do pages change? o oooo How often do you revisit? o oooo Content Changes 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

  8. 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%

  9. 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 8% 17% 51+%

  10. Perceptions of Change Reinforced • Change by page type • Pages that change a lot  change more • Pages that change a little  change less News pages Message boards, forums, news groups Search engine results Blogs you read Change a lot Pages with product information Wikipedia pages Company homepages Personal home pages of people you know Reference pages (dictionaries, yellow pages, maps) Change little

  11. Affects of Highlighting Change • People revisit Web pages more • The pages revisited change more • Perceptions of change are reinforced

  12. 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. DiffIE • Teevan, Dumais, Liebling & Hughes. Changing how peopleview 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

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