http://forum.stanford.edu/events/2012mobi.php Title: Location and Language in Social Media Ed H. Chi Staff Research Scientist, Google Research (work done at [Xerox] PARC) Abstract: Despite the widespread adoption of social media internationally, little research has investigated the differences among users of different languages. Moreover, we know relatively little about how people reveal their location information. In this talk, I will outline our recent characterization studies on how users of differing geographical locations and languages use social media. First, on geographical location: We found that 34% of users did not provide real location information in Twitter, frequently incorporating fake locations or sarcastic comments that can fool traditional geographic information tools. We performed a simple machine learning experiment to determine whether we can identify a user’s location by only looking at what that user tweets. Second, on language, Examining users of the top 10 languages, we discovered cross-language differences in adoption of features such as URLs, hashtags, mentions, replies, and retweets. We discuss our work’s implications for research on large-scale social systems and design of cross-cultural communication tools. Homepage: edchi.net Speaker Bio: Ed H. Chi is a Staff Research Scientist at Google. Until recently, he was the Area Manager and a Principal Scientist at Palo Alto Research Center's Augmented Social Cognition Group. He led the group in understanding how Web2.0 and Social Computing systems help groups of people to remember, think and reason. Ed completed his three degrees (B.S., M.S., and Ph.D.) in 6.5 years from University of Minnesota, and has been doing research on user interface software systems since 1993. He has been featured and quoted in the press, including the Economist, Time Magazine, LA Times, and the Associated Press. With 20 patents and over 90 research articles, his most well-known past project is the study of Information Scent --- understanding how users navigate and understand the Web and information environments. He also led a group of researchers at PARC to understand the underlying mechanisms in online social systems such as Wikipedia and social tagging sites. He has also worked on information visualization, computational molecular biology, ubicomp, and recommendation/search engines, and has won awards for both teaching and research. In his spare time, Ed is an avid Taekwondo martial artist, photographer, and snowboarder.

1. Stanford Mobi Social Workshop 2012 | Invited Talk !
Location and Language Use in Social Media!
!
Ed H. Chi!
!
Google
Research!
!
Work done while
at Palo Alto
Research Center
(Xerox PARC)!
!
!
2012-04-04 Stanford Mobi Social Workshop 2012 Invited Talk 1

2. What can you do with all this data?
Google Trends
Trendalyzer
Big Data Analytics!!
Google Analytics
Google Website
Optimizer
2012-04-04 Stanford Mobi Social Workshop 2012 Invited Talk 2

3. Model-Driven and Living Laboratory Approach
Characterization Unlock Understanding Intelligent UI
and Modeling of Collective Intelligence and Data-mining
Productization
Living Laboratory Applications / Products
2012-04-04 Stanford Mobi Social Workshop 2012 Invited Talk 3

4. !"#$%&#'()*+,(+,(-#./(
2011-07-06 CSCL 2011 Keynote | Ed H. Chi

5. 2011-03-20 Adobe Distinguished Lecture 5

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2011-03-20 Adobe Distinguished Lecture 6

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2012-04-04 Stanford Mobi Social Workshop 2012 Invited Talk 8

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2012-04-04 Stanford Mobi Social Workshop 2012 Invited Talk 10

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2012-04-04 Stanford Mobi Social Workshop 2012 Invited Talk 11

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2012-04-04 Stanford Mobi Social Workshop 2012 Invited Talk 12

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2012-04-04 Stanford Mobi Social Workshop 2012 Invited Talk 13

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2012-04-04 Stanford Mobi Social Workshop 2012 Invited Talk 14

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2012-04-04 Stanford Mobi Social Workshop 2012 Invited Talk 15

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2012-04-04 Stanford Mobi Social Workshop 2012 Invited Talk 16

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2012-04-04 Stanford Mobi Social Workshop 2012 Invited Talk 17

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2012-04-04 Stanford Mobi Social Workshop 2012 Invited Talk 18

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2012-04-04 Stanford Mobi Social Workshop 2012 Invited Talk 19

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2012-04-04 Stanford Mobi Social Workshop 2012 Invited Talk 20

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2012-04-04 Stanford Mobi Social Workshop 2012 Invited Talk 21

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2012-04-04 Stanford Mobi Social Workshop 2012 Invited Talk 22

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2012-04-04 Stanford Mobi Social Workshop 2012 Invited Talk 23

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2011-03-20 Adobe Distinguished Lecture 24

25. 2012-04-04 Stanford Mobi Social Workshop 2012 Invited Talk 25

26. 2012-04-04 Stanford Mobi Social Workshop 2012 Invited Talk 26

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2012-04-04 Stanford Mobi Social Workshop 2012 Invited Talk 27

28. Backstrom et al. 2010
2012-04-04 Stanford Mobi Social Workshop 2012 Invited Talk 28

29. Assumptions about the Location Field!
1. Strongly-typed geo information!
2. Little noise!
3. Good precision!
2012-04-04 Stanford Mobi Social Workshop 2012 Invited Talk 29!

30. 27.7 million
English Tweets 4.6 million 990K+ active
(collected early Twitter Users Twitter users
2010)
Removed
Automatically Randomly Extracted Their
Populated Lat/ Sampled Location Field
Lon Entries 10,000 Entries Entries
(1154)
8846 Manually Entered Twitter Location Field
Entries
2012-04-04 Stanford Mobi Social Workshop 2012 Invited Talk 30

31. 8846 Manually Entered Twitter Location
Field Entries
Two Coders
Powered by human knowledge, the Internet, friends + family, etc.
89%+ Agreement
2012-04-04 Stanford Mobi Social Workshop 2012 Invited Talk 31

32. Study 1: “Geographicness”
!
data quality of the location field
!
“850 n.benson ave!
upland ca”!
18%
Nothing Entered “JoviLand, CA”
!
“Middle Earth”
!
“San Francisco”
!
“Global Citizen”
! 16%!
Non-Valid Geographic! 66% “New Mexico”
!
Information!
“The Moon”
! Some Valid Geographic
Information
“the panhandle”
!
“Worldwide”
!
“Novi Sad,
Serbia, Europe”
!
“kcmo – call the
popo” !
2012-04-04 Stanford Mobi Social Workshop 2012 Invited Talk

33. Study 1: Non-Geo Information
types of non-geographic information entered into the location field
Information Type # of Users
Popular Culture Reference 195 (12.9%)
Privacy-Oriented 18 (1.2%)
Insulting or Threatening to Reader 69 (4.6%)
Non-Earth Location 75 (5.0%)
Negative Emotion Towards Current Location 48 (3.2%)
Sexual in Nature 49 (3.2%)
2012-04-04 Stanford Mobi Social Workshop 2012 Invited Talk 33

34. Study 1: Non-Geo Information
types of non-geographic information entered into the location field
Information Type # of Users
Popular Culture Reference 195 (12.9%)
Privacy-Oriented 18 (1.2%)
Insulting or Threatening to Reader 69 (4.6%)
Non-Earth Location 75 (5.0%)
Negative Emotion Towards Current Location 48 (3.2%)
Sexual in Nature 49 (3.2%)
2012-04-04 Stanford Mobi Social Workshop 2012 Invited Talk 34

35. Study 1: Popular Culture References
Non-geographic information in the location field in user’s profiles
“BieberTown”
“My World”
“belieber wonderland”
“JaeJoongs heart”
“Next to Waldo :D”
“somewhere in Glambertville”
“Los Angeles, 2019 (GET IT?)”
“Schrute Farms”
2012-04-04 Stanford Mobi Social Workshop 2012 Invited Talk 35

36. Study 1: Non-Geo Information
types of non-geographic information entered into the location field
Information Type # of Users
Popular Culture Reference 195 (12.9%)
Privacy-Oriented 18 (1.2%)
Insulting or Threatening to Reader 69 (4.6%)
Non-Earth Location 75 (5.0%)
Negative Emotion Towards Current Location 48 (3.2%)
Sexual in Nature 49 (3.2%)
2012-04-04 Stanford Mobi Social Workshop 2012 Invited Talk 36

37. Study 1: Privacy References
Non-geographic information in the location field in user’s profiles
“Stalker City”
“Stalking me here isnt enough?”
“MindingMyOwn”
“For me to know n u to find out”
“NONE YA BISNESS”
“UM…STALKER!!”
“kgb answers”
2012-04-04 Stanford Mobi Social Workshop 2012 Invited Talk 37

38. Study 1: Implications
STRONGLY-TYPED
GEOGRAPHIC
INFORMATION
REQUIRED
Geocoder
Latitude and
Longitude
Coordinates
2012-04-04 Stanford Mobi Social Workshop 2012 Invited Talk 38

39. Study 1: Quality Implications
STRONGLY-TYPED
GEOGRAPHIC
INFORMATION
REQUIRED
16% Geocoder
Non-Valid Geographic
Information
Latitude and
Longitude
Coordinates
2012-04-04 Stanford Mobi Social Workshop 2012 Invited Talk 39

40. Study 1: Quality Implications
16%
Yahoo! Geocoder
Non-Valid Geographic
Information
“Stalker City”, “NONE YA
BISNESS”, “Justin Biebers
Heart”, “The Void”, “Redneck
Hell”, “In the Middle of
Nowhere”, “yer mum”,
“BSNBC”, “in God’s Graces’,
etc…
2012-04-04 Stanford Mobi Social Workshop 2012 Invited Talk 40

41. “Loserville :)”
(-71.397524, 42.28904)
2012-04-04 Stanford Mobi Social Workshop 2012 Invited Talk 41

42. “With God”
(19.13683,47.705132)
2012-04-04 Stanford Mobi Social Workshop 2012 Invited Talk 42

43. “Justin Biebers heart!”
(-91.700189, 36.328785)
2012-04-04 Stanford Mobi Social Workshop 2012 Invited Talk 43

44. 66%
Some Valid Geographic
Information
!
2012-04-04 Stanford Mobi Social Workshop 2012 Invited Talk 44

45. 27.7 million
English Tweets 4.6 million 990K+ active
(collected early Twitter Users Twitter users
2010)
Removed
Automatically Randomly Extracted Their
Populated Lat/ Sampled Location Field
Lon Entries 10,000 Entries Entries
(1154)
8846 Manually Entered Twitter Location Field
Entries
2012-04-04 Stanford Mobi Social Workshop 2012 Invited Talk 45

46. 33.687456,-84.244945
Seriously?
1154
users?
2012-04-04 Stanford Mobi Social Workshop 2012 Invited Talk 46

47. 2012-04-04 Stanford Mobi Social Workshop 2012 Invited Talk 47

48. Position Information
Implicitly
Self-reported
Sensor-based
Revealed!
Global Positioning System
(GPS)
WiFi Access Point
Cell Phone Towers
2012-04-04 Stanford Mobi Social Workshop 2012 Invited Talk 48

49. Study 2: Country Experiments
Uniform Sampling
Naïve Bayes
United States?
Classifier
Canada?
United Kingdom?
Australia?
72.10% Accuracy
2.91x better than random
2012-04-04 Stanford Mobi Social Workshop 2012 Invited Talk 49

50. Study 2: Country Experiments
Demographically Proportional Sampling
Naïve Bayes
United States?
Classifier
Canada?
United Kingdom?
Australia?
88.86% Accuracy
1.08x better than random
2012-04-04 Stanford Mobi Social Workshop 2012 Invited Talk 50

51. Study 2: State Experiments
Uniform Sampling
California?
Naïve Bayes
Arkansas?
Classifier
New York?
Washington?
Texas?
…
30.28% Accuracy
5.45x better than random
2012-04-04 Stanford Mobi Social Workshop 2012 Invited Talk 51

52. Study 2: State Experiments
Demographically Proportional Sampling
California?
Naïve Bayes
Arkansas?
Classifier
New York?
Washington?
Texas?
…
27.31% Accuracy
1.81x better than random
2012-04-04 Stanford Mobi Social Workshop 2012 Invited Talk 52

53. Study 2: Predictive Words
Word Geography Predictiveness
calgary Canada 419.42
brisbane Australia 137.29
coolcanuck Canada 78.28
afl Australia 56.24
clegg UK 35.49
cbc Canada 29.40
yelp USA 19.80
Word Geography Predictiveness
elk Colorado 90.74
redsox Massachusetts 41.18
biggbi Michigan 24.26
gamecock South Carolina 16.00
crawfish Louisiana 14.87
2012-04-04 Stanford Mobi Social Workshop 2012 Invited Talk 53

54. 1.81x better than random
5.45x better than random
1.08x better than random
2.91x better than random
Tweets Have Implicit
Location Information
2012-04-04 Stanford Mobi Social Workshop 2012 Invited Talk 54

55. This needs to be
considered in the
context of
implicit location
disclosure!
2012-04-04 Stanford Mobi Social Workshop 2012 Invited Talk 55

56. Contributions
!
1. First characterization study of user location
field behavior !
2. Location field behavior is much more complex
than has been assumed!
3. The complexity has implication for geography-
related HCI technologies!
4. Location field behavior must be considered
along with implicit disclosure behavior.!
!
2012-04-04 Stanford Mobi Social Workshop 2012 Invited Talk 56!

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2012-04-04 Stanford Mobi Social Workshop 2012 Invited Talk 57

58. H,#"(7#*25+0"(+1(:0;+2$(-#&+2(
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2012-04-04 Stanford Mobi Social Workshop 2012 Invited Talk 58

59. )*218(?0%h(
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2012-04-04 Stanford Mobi Social Workshop 2012 Invited Talk 59