Describing & Understanding Neighborhood Characteristics through Online Social Media
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Duo-talk by Mohamed Kafsi & Henriette Cramer at WWW'15 in Florence. We describe and compare San Francisco and New York by applying a new geographic hierarchical model to 8M geotagged flickr photos. We compare machine and human reasoning and infer implications for model extensions as well as presentation in user-facing applications. Mind the gap! Mohamed Kafsi, Henriette Cramer, Bart Thomee, Ayman Shamma. Describing and understanding neighborhood characteristics through social media. https://drive.google.com/file/d/0B70N5TMgSSKWakRuQmdRcDBXWnc/view?usp=sharing
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Describing & Understanding Neighborhood Characteristics through Online Social Media
- 1. (CC BY 2.0) Rachel Elaine, flic.kr/p/fw8HSn Describing & Understanding Neighborhood Characteristics through Online Social Media Mohamed Kafsi Henriette Cramer Bart Thomee, David A. Shamma 1
- 2. CC Steve, flic.kr/p/5tm2Ci 2 What’s unique about Florence? What’s similar to other cities?
- 3. CC Pedro, flic.kr/p/85QqvB CC Mitch Altman, flic.kr/p/ffHDeY Lisboa Paris in Florence? Which neighborhoods have characteristic street art?
- 4. masses of geotagged, user community-generated content
- 5. User-generated content * finding, describing, defining locales & boundaries (e.g. ZoneTag, TagMaps ‘07, Livehoods'12, Thomee & Rae’13 etc.) CC Momo, flic.kr/p/6bKMtX
- 6. But #1: locally frequent is not always specifically descriptive blackandwhite, gotham, tribeca Brett Weinstein, flic.kr/p/RHWan 6
- 7. Bert Kaufmann, flic.kr/p/dEfd12 #2 regions within regions Is ‘desert’ a descriptor of Las Vegas?or rather the surrounding area? 7
- 8. Problem: let’s describe neighborhoods with flickr content go beyond local, compare neighborhoods focus on user-defined noisy tags -ignore the pixels- build model to distinguish ‘specifically descriptive’ local content work with the geo-hierarchies that people are familiar with compare with human reasoning (interviews & survey)
- 9. Goal: -Find specific descriptions of pre-defined regions -Quantify their uniqueness -Map similar regions Idea: -Define any geographical hierarchy of regions -Quantify the descriptiveness of tags with respect to a given geographical level Geographical hierarchy of tags 9
- 10. Country Country City A City B City C Cities Hood B.1 Hood B.2 Hood 10
- 12. Country Country City A City B City C Cities Hood B.1 Neighborho od B.2 Hood Randomly sample tags from the nodes along the path from leaf to root 12
- 13. + Distribution of tags in neighborhood n θcountry(n) πcountry(n) θn πn θcity(n) πcity(n) 13
- 14. 14 (cc by 2.0) S J Pinkney, flic.kr/p/8cNAgd (CC BY 2.0) !STORAX, flic.kr/p/39Wstq SF & Manhattan - Sample of 8M geo-tagged photos - 20M tags, - vocabulary of 8000 unique tags
- 15. Probability of tag t in neighborhood n p(t|n) = X v2Rn ✓v(t) p(z = d(v) | n) depth of node v path from the leaf n to the root of the geo-tree multinomial distribution associated with node v 15
- 16. Training - Expectation-Maximization to learn the model’s parameters - Fast convergence - Scales. Worst case running time O(N V D) 16
- 17. Results: assigning tags to a level 17
- 18. Mission, SF California Mission SF USA Graffiti Art Mural Valencia Food Car David McSpadden, flic.kr/p/oVLorr Most frequent tags 18 Country: 0.06 City: 0.33 Neighborhood: 0.61
- 19. California Mission SF USA Graffiti Art Mural Valencia Food Car Mission, SF Most frequent tags 19 Country: 0.06 City: 0.33 Neighborhood: 0.61 David McSpadden, flic.kr/p/oVLorr
- 20. New York Manhattan USA Midtown Skyscraper Time square Light Moma Broadway Rockfeller Midtown South, Manhattan CC Jeffrey Zeldman, flic.kr/p/s1eE5W Most frequent tags 20 Country: 0.21 City: 0.32 Neighborhood: 0.47
- 21. New York Manhattan USA Midtown Skyscraper Time square Light Moma Broadway Rockfeller Midtown South, Manhattan Most frequent tags 21 Country: 0.21 City: 0.32 Neighborhood: 0.47 CC Jeffrey Zeldman, flic.kr/p/s1eE5W
- 22. 1. Where do I find tag t? (i.e. where is tag t most locally descriptive) 22
- 23. Food 23 Hipster Mapping Tags in San Francisco
- 24. 2. where are the unique ‘hoods? quantifying uniqueness 24
- 25. 25 Uniqueness
- 26. 3. What’s the east-village of SF? mapping & comparing neighborhoods 26
- 27. San Francisco Manhattan Top common tags Mission East Village (0.23) graffiti, food, restaurant, mural, bar Golden gate park Washington heights (0.26) Upper west side (0.22) park, museum, nature, flower, bird Financial district Battery park (0.29) Midtown Manhattan (0.27) downtown, building, skyscraper, city, street Chinatown Chinatown (0.85) Chinatown, Chinese, downtown, dragons, lantern Castro West village (0.06) park, gay, halloween, pride, bar sim(n, n0 ) = PV t=1 ✓n(t) ✓n0 (t) qPV t=1 ✓n(t)2 qPV t=1 ✓n0 (t)2 27
- 28. Does it match human reasoning? presentation -or- model adaptation
- 29. locals’ reasoning in classifying tags’ local specificity 10 interviews Survey: 22 human classifiers 3 neighborhoods: classify 32 tags 1291 tag classifications ###################### san-francisco, Castro/Upper Market ###################### sfmoma sf embarcadero baseball san mission market tram rainbow muni pride city sanfrancisco gay california alcatraz streetcar francisco male dolorespark castro soma church sign street theater flag usa dolores night movie halloween ############################ san-francisco, Marina ############################# t sanfrancisco flowers bird california usa night embarcadero street bridge presidio pond explorato olumns mason fort golden bay san financi marina sfmoma
- 30. Principle 1. While users’ tagging is personal, varied, local; (e.g. Naaman et al., 07) - the community generates similar meta-content (e.g. Rost et al., 2013)
- 31. 2. Personal experiences shape classifications this slides features 3 different churches. CC Justin Pickard, flic.kr/p/6hWpoa CC torbakhopper, flic.kr/p/9PYSjx
- 32. (CC BY 2.0) Dustin Gaffke, flic.kr/p/aCvuxP No human ground truth. SF: North Beach? P1: ’party land!’ P2: ‘I don’t believe there’s much of a nightlife there’ 3. Teaching opportunity? ‘mistakes’ or perspectives? 32
- 33. Proposed geo-model extensions (i.e. humans aren’t wrong)
- 34. 1. even when using existing regions, detecting sub-regions is important this is (not) North Beach (CC BY 2.0) David Ohmer, flic.kr/p/2xT9rU Most ‘rejected’ by our human classifiers: Fisherman’s Wharf in North Beach in our official neighborhood dataset… but it’s its own thing.
- 35. San Francisco neighborhood map (1960) From The Urban Aesthetic: Evolution of a Survey System. (CC BY 2.0) Eric Fischer, flic.kr/p/dTxhBg Night, The Mission: “before…you wouldn’t be caught there at night-time… 20 years ago it was a different neighborhood.” 2. boundaries, character, data change over time
- 36. (CC BY 2.0) Σταύρος, flic.kr/p/6ZZX6A 3. Topography & closeness matters: make neighborhood boundaries permeable Geographic Hierarchical model with Adjacency, extension.
- 37. (CC BY 2.0) Rachel Elaine, flic.kr/p/fw8HSn Mohamed Kafsi - mohamed.kafsi@epfl.ch Henriette Cramer @hsmcramer henriette@yahoo-inc.com Bart Thomee bthomee@yahoo-inc.com David A. Shamma shamma@yahoo-inc.com SUMMARIZING: Geographical Hierarchy Model - resilient & scalable probabilistic model (with adjacency extension) - applied on sample Flickr photos SF & NY - we can describe & compare neighborhoods, quantify uniqueness & similarity - mind the individual human interpretation gap: we present an aggregate view based on community-generated content. 37