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Photos, Mobile, Location and the Social Media Cycle

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A talk at NYU / ITP

A talk at NYU / ITP

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  • Good timing - took 50mins with questions in the middle, and a bit hurried at the end. http://slideshare.net/mor
  • Transcript

    • 1. Photos, Mobile, Location and the Social Media Cycle 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 (e.g., `home’)
      • Tags my friends used in this context (e.g., `Yahoo! Sunnyvale’)
      • Tags other people used in this context (`Golden Gate’, `ITP’, ‘Mor Naaman’, `This talk sucks’)
    • 36. Where do tags come from?
      • Stuff around you:
        • Yahoo! Local
        • Upcoming.org (`Mor Naaman @ ITP’, `NYU’): USE IT!
      • 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