1. Research Update on WebPlaces: Application of Implicit Networks Danyel Fisher Human-Centered Computing Retreat Summer, 1999

2. Overview WebPlaces –Motivation –Places on the Web –Implicit Networks –Ambiguities in Places –Technical Details Questions?

3. Motivation People spend lots of time on web –More generally, “doing stuff with documents” Alone and non-interactive –A few sites have added local discussions, chat rooms, and similar… no universals.

4. Places vs Spaces Erickson, Dourish… and others “Spaces” (Latitude 37.77003, Longitude -122.446882) –URLs. Locations. Bits. Dry. Empty. (“Cold”? “Hard”?) as opposed to “Places” (“Haight and Ashbury”) –Awareness. People. Connections. Ideas. Stuff. Interactive. (“Warm”? “Soft”?)

5. From This, An Answer: Add “Placeness” to the web. Leverage community experience: –Get other people’s ideas, too! Use Technology from –Recommendation Systems Who do I want to talk to? –Groupware How can I share ideas?

6. WebPlaces IBM Research USER group, summer ‘98. Intern project Who else is in the same place? How can you get in touch with them? What can you do with them? More than just URL: want a cluster of ideas

7. Affordances in a Place See other users Chat Follow Hide

8. WebPlaces Three users in nearby places...

9. Selection and Commands

10. Uses Internet: Find other people with the same interests Corporate Intranet: Help employees share knowledge. Academic nets and databases: link users to learn from each other

11. Working Issues and Details Mappings from URLs and Users to Places Ambiguities in Distance Measures Technical Details –Web Proxies –Architecture of WebPlaces with WBI

12. Mapping from URL to Places Straight URL? Pure Net Topology? –For example, Canny’s web page is one link away from mine. –Sophisticated Topology: a “site” is a 2-clan of pages on the same server Information Retrieval –Search engine words convey similarity –Tie similarity to adjacency

13. (URL, User)  Place User Paths –Related paths through information space implies related interest. User-Defined –Even if the system doesn’t know that two pages are the “same place,” the user does. Can we accommodate explicit preferences?

14. Implicit Structure Social Networking Notion: –Graph of information and users –Changes as users travel, communicate –Reflects users’ shared interests… The map changes as you travel and interact with it. –Is this confusing?

15. Place Ambiguity: Distance Measures If the internet looks like the map on top, then it is hard to decide which web sites are close together and how to cluster them. If the internet “looks like” the map below, than the clustering is more obvious.

16. More Ambiguities These illustrations show two possible arrangements of “places” over a single network. In each case, the algorithm is that “everything at a distance of 1 is in the same place.” Clearly, we need a less simple-minded algorithm.

17. Web Proxies Modify web content on-the-fly Change HTTP requests going out… …and bits coming back in. Typical Uses: –Transcoding formats (Shrink image size?) –Tracking User Interests –Filters (e.g. Kidproofing)

18. Architecture

19. References Tom Erickson’s “Babble,” CHI 99 Paper Judith Donath, “Crowds at Web Sites” MSR’s “Data Mountains,” CHI 99 Commercial Product –ThirdVoice: www.thirdvoice.com Paul Maglio and Rob Barrett, IBM Research –WWW8 Conference Poster

20. Questions? Project Links –WebPlaces –Talk of the Net: Melissa Virus –Social Networks and Mailing Lists http://www.cs.berkeley.edu/~danyelf Search engine: “Danyel Fisher”http://www.cs.berkeley.edu/~danyelf