Stanford Mobi Social Workshop 2012 | Invited Talk                  !Location and Language Use in Social Media!!Ed H. Chi!!...
What can you do with all this data? Google Trends                                  Trendalyzer Big Data Analytics!!       ...
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2011-03-20   Adobe Distinguished Lecture   5
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2012-04-04   Stanford Mobi Social Workshop 2012 Invited Talk   25
2012-04-04   Stanford Mobi Social Workshop 2012 Invited Talk   26
b2"?+19(T0"Q,(0>(<0;2=+01(T+#$&(                                                       )F+==#"(                           ...
Backstrom et al. 20102012-04-04   Stanford Mobi Social Workshop 2012 Invited Talk   28
Assumptions about the Location Field!  1.  Strongly-typed geo information!  2.  Little noise!  3.  Good precision! 2012-04...
27.7 millionEnglish Tweets                     4.6 million                         990K+ active(collected early           ...
8846 Manually Entered Twitter Location                          Field Entries                                    Two Coder...
Study 1: “Geographicness”                                            !                                              data q...
Study 1: Non-Geo Information              types of non-geographic information entered into the location field    Informati...
Study 1: Non-Geo Information              types of non-geographic information entered into the location field    Informati...
Study 1: Popular Culture References               Non-geographic information in the location field in user’s profiles     ...
Study 1: Non-Geo Information              types of non-geographic information entered into the location field    Informati...
Study 1: Privacy References             Non-geographic information in the location field in user’s profiles               ...
Study 1: Implications                             STRONGLY-TYPED                               GEOGRAPHIC                 ...
Study 1: Quality Implications                                    STRONGLY-TYPED                                      GEOGR...
Study 1: Quality Implications       16%                                              Yahoo! Geocoder      Non-Valid Geogra...
“Loserville :)”                  (-71.397524, 42.28904)2012-04-04   Stanford Mobi Social Workshop 2012 Invited Talk   41
“With God”                   (19.13683,47.705132)2012-04-04   Stanford Mobi Social Workshop 2012 Invited Talk   42
“Justin Biebers heart!”                (-91.700189, 36.328785)2012-04-04   Stanford Mobi Social Workshop 2012 Invited Talk...
66%                                          Some Valid Geographic                                              Informatio...
27.7 millionEnglish Tweets                     4.6 million                         990K+ active(collected early           ...
33.687456,-84.244945                          Seriously?                                                               115...
2012-04-04   Stanford Mobi Social Workshop 2012 Invited Talk   47
Position Information                                                              Implicitly                              ...
Study 2: Country Experiments                      Uniform Sampling                               Naïve Bayes              ...
Study 2: Country Experiments             Demographically Proportional Sampling                                   Naïve Bay...
Study 2: State Experiments                      Uniform Sampling                                                          ...
Study 2: State Experiments             Demographically Proportional Sampling                                              ...
Study 2: Predictive WordsWord                              Geography                    Predictivenesscalgary             ...
1.81x better than random               5.45x better than random               1.08x better than random               2.91x...
This needs to be                                              considered in the                                           ...
Contributions                         !1.  First characterization study of user location    field behavior !2.  Location fie...
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Location and Language in Social Media (Stanford Mobi Social Invited Talk)

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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.

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Location and Language in Social Media (Stanford Mobi Social Invited Talk)

  1. 1. Stanford Mobi Social Workshop 2012 | Invited Talk !Location and Language Use in Social Media!!Ed H. Chi!!GoogleResearch!!Work done whileat Palo AltoResearch Center(Xerox PARC)!!! 2012-04-04 Stanford Mobi Social Workshop 2012 Invited Talk 1
  2. 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. 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. 4. !"#$%&#()*+,(+,(-#./(2011-07-06 CSCL 2011 Keynote | Ed H. Chi
  5. 5. 2011-03-20 Adobe Distinguished Lecture 5
  6. 6. !#",012$(!"03$#(!  !"#$%&"(")*+%,%-." /&-01&0%#"21%-./34" 5  6-0/*#7" 5  8&-)&,*-" 5  9&-.:-%#%"!  ;-)"(":.7%,"/&-01&0%#" <&)/3=" 5  >&*?&-%#%" 5  @%,A&-" 5  B&$&-%#%"2011-03-20 Adobe Distinguished Lecture 6
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  20. 20. S%Q6#"(0>(7+$+19%2$(7"08#",( E( J( P( I( S( D( K( F( G(J( 140,730"P( 488,545( 13,228"I( 230,023" 4,825" 29,405"S( 359,117" 10,139" 112,524" 36,068"D( 150,041" 6,383" 30,855" 34,906" 30,916"K( 19,722" 6,384" 906( 2,014" 1,109" 972(F( 194,931" 10,463" 53,607" 34,586" 49,445" 33,568" 1,244"(G 110,748" 6,053" 22,106" 21,471" 21,989" 22,162" 786( 24,763"(M 148,365" 4,208" 31,184" 135,427" 31,967" 29,331" 1,518" 30,257" 18,301" 2012-04-04 Stanford Mobi Social Workshop 2012 Invited Talk 20
  21. 21. :*2"+19(H[<,(Z;"0,,(<219%29#,( E( J( P( I( S( D( K( F( G( M(E 3,013" 18,399( 985" 4,986" 1,144" 212" 1,791" 1,647" 540"J( 3,013" 77" 37" 58" 29" 43" 59" 46" 18"P 18,399( 77" 74" 1,644" 198" 2" 453" 168" 123"I( 985" 37" 74" 67" 64" 1" 53" 38" 279"S 4,986" 58" 1,644" 67" 139" 0( 286" 139" 53"D 1,144" 29" 198" 64" 139" 2" 112" 126" 48"K 212" 43" 2" 1" 0( 2" 3" 3" 1"F( 1,791" 59" 453" 53" 286" 112" 3" 157" 53"G 1,647" 46" 168" 38" 139" 126" 3" 157" 40"M 540" 18" 123" 279" 53" 48" 1" 53" 40" 2012-04-04 Stanford Mobi Social Workshop 2012 Invited Talk 21
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  23. 23. 4QC$+;2=+01,(!  !-)*E&.:,#":P"E:--%E.*:-"#.,%-0.7"<%.?%%-" /&-01&0%#" 5  g1A<%,":P"<*/*-01&/"<,:%,#" 5  ;E.#":P"<,:%,&0%="#7&,*-0"TVD#"_"7&#7.&0#"!  6-0/*#7"?%//"E:--%E.%)".:":.7%,#G"&-)"A&3" P1-E.*:-"&#"&"71<"!  g%%)".:"*A$,:H%"E,:##S/&-01&0%" E:AA1-*E&.*:-#" ?2012-04-04 Stanford Mobi Social Workshop 2012 Invited Talk 23
  24. 24. `<0;2=+01,a(:+912$,(+1(:0;+2$(-#&+2( P#;*=Y(P019Y(:%*(B(A*+Y(AP4(WEDD( 2011-03-20 Adobe Distinguished Lecture 24
  25. 25. 2012-04-04 Stanford Mobi Social Workshop 2012 Invited Talk 25
  26. 26. 2012-04-04 Stanford Mobi Social Workshop 2012 Invited Talk 26
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  28. 28. Backstrom et al. 20102012-04-04 Stanford Mobi Social Workshop 2012 Invited Talk 28
  29. 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. 30. 27.7 millionEnglish Tweets 4.6 million 990K+ active(collected early Twitter Users Twitter users 2010) RemovedAutomatically Randomly Extracted TheirPopulated Lat/ Sampled Location Field Lon Entries 10,000 Entries Entries (1154) 8846 Manually Entered Twitter Location Field Entries2012-04-04 Stanford Mobi Social Workshop 2012 Invited Talk 30
  31. 31. 8846 Manually Entered Twitter Location Field Entries Two Coders Powered by human knowledge, the Internet, friends + family, etc. 89%+ Agreement2012-04-04 Stanford Mobi Social Workshop 2012 Invited Talk 31
  32. 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. 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. 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. 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. 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. 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. 38. Study 1: Implications STRONGLY-TYPED GEOGRAPHIC INFORMATION REQUIRED Geocoder Latitude and Longitude Coordinates2012-04-04 Stanford Mobi Social Workshop 2012 Invited Talk 38
  39. 39. Study 1: Quality Implications STRONGLY-TYPED GEOGRAPHIC INFORMATION REQUIRED 16% GeocoderNon-Valid Geographic Information Latitude and Longitude Coordinates 2012-04-04 Stanford Mobi Social Workshop 2012 Invited Talk 39
  40. 40. Study 1: Quality Implications 16% Yahoo! Geocoder Non-Valid Geographic Information“Stalker City”, “NONE YABISNESS”, “Justin BiebersHeart”, “The Void”, “RedneckHell”, “In the Middle ofNowhere”, “yer mum”,“BSNBC”, “in God’s Graces’,etc… 2012-04-04 Stanford Mobi Social Workshop 2012 Invited Talk 40
  41. 41. “Loserville :)” (-71.397524, 42.28904)2012-04-04 Stanford Mobi Social Workshop 2012 Invited Talk 41
  42. 42. “With God” (19.13683,47.705132)2012-04-04 Stanford Mobi Social Workshop 2012 Invited Talk 42
  43. 43. “Justin Biebers heart!” (-91.700189, 36.328785)2012-04-04 Stanford Mobi Social Workshop 2012 Invited Talk 43
  44. 44. 66% Some Valid Geographic Information !2012-04-04 Stanford Mobi Social Workshop 2012 Invited Talk 44
  45. 45. 27.7 millionEnglish Tweets 4.6 million 990K+ active(collected early Twitter Users Twitter users 2010) RemovedAutomatically Randomly Extracted TheirPopulated Lat/ Sampled Location Field Lon Entries 10,000 Entries Entries (1154) 8846 Manually Entered Twitter Location Field Entries2012-04-04 Stanford Mobi Social Workshop 2012 Invited Talk 45
  46. 46. 33.687456,-84.244945 Seriously? 1154 users?2012-04-04 Stanford Mobi Social Workshop 2012 Invited Talk 46
  47. 47. 2012-04-04 Stanford Mobi Social Workshop 2012 Invited Talk 47
  48. 48. Position Information Implicitly Self-reported Sensor-based Revealed! Global Positioning System (GPS) WiFi Access Point Cell Phone Towers2012-04-04 Stanford Mobi Social Workshop 2012 Invited Talk 48
  49. 49. Study 2: Country Experiments Uniform Sampling Naïve Bayes United States? Classifier Canada? United Kingdom? Australia? 72.10% Accuracy 2.91x better than random2012-04-04 Stanford Mobi Social Workshop 2012 Invited Talk 49
  50. 50. Study 2: Country Experiments Demographically Proportional Sampling Naïve Bayes United States? Classifier Canada? United Kingdom? Australia? 88.86% Accuracy 1.08x better than random2012-04-04 Stanford Mobi Social Workshop 2012 Invited Talk 50
  51. 51. Study 2: State Experiments Uniform Sampling California? Naïve Bayes Arkansas? Classifier New York? Washington? Texas? … 30.28% Accuracy 5.45x better than random2012-04-04 Stanford Mobi Social Workshop 2012 Invited Talk 51
  52. 52. Study 2: State Experiments Demographically Proportional Sampling California? Naïve Bayes Arkansas? Classifier New York? Washington? Texas? … 27.31% Accuracy 1.81x better than random2012-04-04 Stanford Mobi Social Workshop 2012 Invited Talk 52
  53. 53. Study 2: Predictive WordsWord Geography Predictivenesscalgary Canada 419.42brisbane Australia 137.29coolcanuck Canada 78.28afl Australia 56.24clegg UK 35.49cbc Canada 29.40yelp USA 19.80Word Geography Predictivenesselk Colorado 90.74redsox Massachusetts 41.18biggbi Michigan 24.26gamecock South Carolina 16.00crawfish Louisiana 14.872012-04-04 Stanford Mobi Social Workshop 2012 Invited Talk 53
  54. 54. 1.81x better than random 5.45x better than random 1.08x better than random 2.91x better than randomTweets Have ImplicitLocation Information2012-04-04 Stanford Mobi Social Workshop 2012 Invited Talk 54
  55. 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. 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!
  57. 57. :0;+2$(:="#2Q([#,#2";*(!  ;-&/3.*E#" 5  c&E.:,#"*A$&E.*-0",%.?%%.&<*/*.3"IK17"%."&/G"!666"K:E*&/" 9:A$1.*-0"LMNMO" 5  D:E&.*:-"e%/)":P"1#%,"$,:e/%#"IF%E7."%."&/G"9F!"LMNNO" 5  c"921+;(dBZ(6#*25+0",(e!2%$(#=(2$Y(4Af:-UDDg( 5  <219%29#,(%,#&(+1()F+==#"(eP019(#=(2$Y(4Af:-UDDg(!  !A$,:H*-0"K.,%&A"6m$%,*%-E%" 5  >:$*ES<&#%)"#1AA&,*R&.*:-"_"<,:?#*-0":P".?%%.#"Ij%,-#.%*-"%." &/G"T!K>LMNMO( 5  >?%%.",%E:AA%-)&.*:-"I97%-"%."&/G"9F!LMNM"_"9F!LMNNO" 2012-04-04 Stanford Mobi Social Workshop 2012 Invited Talk 57
  58. 58. H,#"(7#*25+0"(+1(:0;+2$(-#&+2(!  T#%,#"&):$."&-)"&)&$."#:E*&/"A%)*&".:"#1*.".7%*,"-%%)#4"!  U%#*0-*-0"P:,".7%"H&,*%.3":P"<%7&H*:,"*#"E,*.*E&/".:".7%" #1EE%##":P"#:E*&/"A%)*&4"!  J7&."?%"/%&,-%)=" 5  D:E&.*:-"e%/)"E&-"<%"n1*.%"%m$,%##*H%h" 5  D&-01&0%"&+%E.#"1#%"E&#%#h" 5  D&-01&0%"E&-"<%"&"<&,,*%,".:"%m$,%##*:-"&-)"*-P:,A&.*:-" <,:%,&0%4"2012-04-04 Stanford Mobi Social Workshop 2012 Invited Talk 58
  59. 59. )*218(?0%h( !  E7*o&EA4:,0" !  7..$=CC%)E7*4-%."2012-04-04 Stanford Mobi Social Workshop 2012 Invited Talk 59

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