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MIT CSAIL HCI Seminar

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    CHECK SOUND! http://slideshare.net/mor

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    MIT CSAIL HCI Seminar - Presentation Transcript

    1. The Community/Individual Cycle: Extracting Knowledge, Creating Applications and Understanding Motivations in Public Photo Collections Mor Naaman Yahoo! Research Berkeley
    2. Attraction Map of Paris
        • Stanley Milgram, 1976.
        • Psychological Maps of Paris
    3. Attraction Map of Paris
        • Y!RB, 2007.
    4. Social Media Cycle How? What? Why?
    5. Talk Outline
      • Wisdom of the tags
        • mining information from geo-tagged photos
      • Applications
        • World Explorer
        • ZoneTag
      • Why we tag
        • A user study of tagging motivations
    6. Information Overload?
        • Flickr “geotagged”
    7. What can we derive?
      • Location-driven model
      • Tag-driven model
      Dataset: (photo_id, user_id, time, latitude, longitude) (photo_id, tag)
    8. Issues to Tackle
      • Noisy data
      • Photographer biases
        • In locations
        • In Tags
      • Wrong data
      Whatever, color, city, spectrum, santa barbara, california, usa, Lookatme, Herbert Bayer Chromatic Gate
    9. Location-driven Modeling
      • Derive meaningful data about map regions
      • E.g., representative tags, photos
    10. Intuition
        • More “activity” in a certain location indicates importance of that location
        • Tag that are unique to a certain location can represent the location better
    11. Translation into simple algorithm
      • Clustering of photos
      • Scoring of tags
        • TF / IDF / UF
    12. Tag Maps - Paris
    13. Tag Maps - SF
    14. Tag Maps - Know this place?
    15. Summary of San Francisco Golden Gate Bridge TransAmerica AT&T Baseball Park Golden Gate Twin Peaks Golden Gate Bay Bridge Ocean Beach Chinatown
    16. Tag-based Modeling
      • Derive meaningful data about individual tags
      • Based on the tag’s metadata patterns
      • E.g., Yahoo! Research Berkeley , CHI2007 .
    17. Tag Maps - Paris - Les Blogs?
    18. Tag Semantics
      • Improved image search through query semantics
      • Automatic place- and event-gazetteers
      • Association of missing time/place data based on tags
      • …
    19. Definitions
      • Event tag: expected to exhibit significant time-based patterns
      • Location tag: expected to exhibit significant place-based patterns
    20. Tag patterns
    21. Tag patterns
    22. Intuition for Solution
      • Burstiness does not work
        • Data too sparse
        • Goodbye, traditional techniques
      • Scale-structure method
        • Examine the “structure” of the tag’s patterns in multiple scales
    23. Scale-structure Identification Entropy = 1.421621 Entropy = 0.766263 Entropy = 0.555915 Entropy = 0.062760
    24. Scale-structure Identification
    25. Outline How? What? Why?
    26. Outline
      • Wisdom of the tags
        • mining information from geo-tagged photos
      • Applications
        • World Explorer
        • ZoneTag
      • Why we tag
        • a user study of tagging
    27. Another Look at the Tag Map
    28. Make it into World Explorer http://tagmaps.research.yahoo.com
    29. ZoneTag(?)
      • How would we:
      • Create/store?
      • Find?
      • Share?
      • Discover?
      “ Everything in the world exists to end up in a photograph” -- Susan Sontag
    30. Why Cameraphones?
      • Numbers, numbers…
      • Programmable
      • Context-aware
      • Network-connected
      • Quality… it’s getting there
        • 500,000,000
      (Source: Future Image Inc.)
    31. Current Mobile Experience
      • Difficult to share (or even save!)
      • Hard to find
        • No context
        • No semantic information
      Current mobile experience?
    32. ZoneTag Experience
      • 2-click upload (same key!)
      • Photo uploaded with location and time metadata
    33. ZoneTag Experience
      • Tagging made easy
        • Tag/annotate your photos from the phone
    34. Where do locations come from?
      • Bluetooth GPS (when available)
      • User-contributed cell tower mapping
    35. Where do tags come from?
      • Tags I used in this context (`home’)
      • Tags my friends used in this context (`Research Meeting’)
      • Tags other people used in this context (`CSAIL HCI Seminar’, ‘Mor Naaman’, `This talk sucks’)
    36. Where do tags come from?
      • Stuff around you:
        • Yahoo! Local
        • Upcoming.org (`Mor Naaman @ CSAIL’, `MIT’)
      • Stuff from you (any RSS 2.0 feed):
        • Calendar ( G, Upcoming.org,… )
        • Favorite hangouts (Wayfaring, Plazes, Socialight)
    37. Suggested Tags “ When I went to upload, there were already all these exotic tags like "Bill Graham Civic Auditorium" and "Bob dylan live" available...it sure was convenient to just select and go…”
    38. Where do tags go?
      • Back to the original RSS items
        • Upcoming.org
      • Action Tags
        • Trigger a call to a web service
          • With parameters
        • Command line - from your phone
      Rotate:right Email:dad Group:zonetag Scanr:document
    39. I’m Too Lazy (you’re not alone)
      • Tagging is the means, not the goal
      • Benefits even if you never tagged a single image
      (Bradley Horowitz, elatable.com) (?) (?) (?)
    40. I’m Too Lazy (you’re not alone)
      • Tagging is the means, not the goal
      • What is the goal?
    41. Outline How? What? Why?
    42. Outline
      • Wisdom of the tags
        • mining information from geo-tagged photos
      • Applications
        • World Explorer
        • ZoneTag
      • Why we tag
        • a user study of tagging
    43. Inspiration
      • Kinberg, Spasojevic, Fleck, Sellen
        • Social uses of camera-phones
      • Nancy van House
        • Research methodology
    44. A few motivating facts
      • ZoneTag used by 500+ users; 50,000+ photos
      • 61% of users - at least one tag per photo on average
        • Some more than five
      • Some users only tag on phone
        • some never tag on phone
    45. A few motivating facts Average Tags, Public ZoneTag Photo: 2.2 Average Tags, Public Shozu Photo: 0.97 Average Tags, Private ZoneTag Photo: 1.85
    46. Tag Affordances on Flickr
      • Displayed next to photo
      • Can be used to search:
        • Your own photos
        • Others’ photos
        • Public photos
    47. Tags on ZoneTag
      • By default, tags are kept from one photo to next
      • Tags used in a location will be suggested in that location
        • To you
        • To others
      • Immediate (i.e., during event)
      • Place name tags added automatically
    48. Why Tag? Bay Bridge, Fog, [San Francisco, 94105]
    49. Why Tag? RedSox, Fenway, GreenMonster, [Boston, 02215], …
    50. Why Tag? Horsetails, handicapped, sign, [Stanford, 94305], …
    51. User Study
      • 13 ZoneTag users (23-45, 9m, 4f)
      • All “taggers” (no use to ask non-taggers why they tag)
      • Structured interviews
    52. Expected…
      • Organization/retrieval
      • Creator/synthesizer/consumer
      • “Tagging for community”
    53. Found…
      • Motivation taxonomy
      • No “synthesizers”
      • Inspired by community
      • Organization/retrieval
      • Creator/Synthesizer
      • “ Tagging for community”
      X
    54. Motivation Taxonomy “ If I tagged ahead of time I can go back and get all my pictures of [my children]…” “… I then think “well, maybe I should tag this” so I can find it again later” “ I’m obsessive-compulsive”
    55. Motivation Taxonomy “ I want at least one hook of association in there that can help me reconstruct…”
    56. Motivation Taxonomy “ I tag photos with what I think might be interesting to other people, stuff I think people will like” “ I know that tagging can connect my photos to activities, and get more interest” “ I want to look at all [my neighborhood’s] tags. … That’s definitely a reason I’m putting these tags in ”
    57. Motivation Taxonomy “… so that my friends can see what I am up to” I can tell my mom [with the tag] “look, we went to…” “ I left reviews of places – like at the airport, when my flight was delayed, I tagged “Aloha Air sucks.”
    58. Breakdown
    59. Suggested Tags
      • Useful for entry, when they “work”
        • Critical mass needed
        • Better filtering
      • Inspire and give example
      • Overtagging may be issue
      “ Tag suggestions were huge for me; they really cut down on typing” “ I thought: this is how I want it to work all the time!” “ This person was in my phone for a month - who is she???” “ I try to use as many suggested tags that apply. … I also use it for auto-completion – I type “s” to get San Francisco”
    60. Tags/Contribution
      • Derived from affordances
        • Social engineering
      • HT06, tagging paper, taxonomy, Flickr, academic article, to read (HyperText 2006)
    61. Open Questions
      • Those who don’t…
      • Better filtering, tag suggestions
      • Longer usage trends
      • Effect of community on usage
      • …
    62. Enough already with this stupid figure!
    63. Final Notes
      • All Flickr/ZT photos, Creative Commons or with author permission
      • http://www.flickr.com/photos/frozenquack/106111967/
      • http://www.flickr.com/photos/bryce_edwards/272002447/
      • http://www.flickr.com/photos/plasticbag/188818779/
      • http://www.flickr.com/photos/groovymother/131591824/
      • http://www.flickr.com/photos/deaneckles/135146357/
    64. Final Notes: APIs For All
      • Everything we can do, you can do (better). Use our APIs:
        • Cell ID translation
        • Suggested Tags
        • TagMaps data
        • TagMaps Widget
        • Extend ZoneTag with RSS feeds, ActionTags
        • …
      • Email me
    65. Thanks
      • With: Shane Ahern, Simon King, Rahul Nair, Nathan Good, Marc Davis, Morgan Ames, Dean Eckles, Alex Jaffe, Tamir Tassa, Jeannie Yang, Tye Rattenbury
      • Read more, follow: http://www.yahooresearchberkeley.com
      • Slides: http://slideshare.net/mor
      • What are you doing this summer?
      • Mor Naaman: mor@yahoo-inc.com
    66. Things We Learned
      • “ Best” feature: 2-click upload
        • Even users that often tag
      • For some, last chance to annotate
      • Mobile tagging - not just experts, not all experts…
    67. Things We Learned
      • Tagging behavior:
        • All over the place
        • More than Shozu
        • Connected to privacy
    68. International Remix
      • Demo?
      • Extracting patterns
    69. International Remix
    70. Key Framing
    71. Reuse Patterns
    72. Reuse Patterns
    73. How to use?
      • How can Intl Remix leverage the community to help the individual?
    74. Flickr Example
      • Distribution of “number of photos in account”
      Number of photos Number of users 6 12 18 … Huh??
    75. Case Study - Flickr
      • Nobody tags other people’s content
      • Why?
      Not collected Not identified Not prominent In user’s account As coming from the tagger In the interface, as “opinion” Not aggregated Can’t “vote” on tag/item pair

    + mormor, 3 years ago

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