1. Location Mining from Online Social Networks
Satyen Abrol
Advisors:
Dr. Latifur Khan
Dr. Bhavani Thuraisingham

2. Location Mining in Online Social Networks
What is the city level home location of a user?

3. Outline Introduction and Problem Statement Different Approaches
Social Graph Based: Our Approaches
Tweethood: Fuzzy k – Closest Friends with Variable Depth
Tweecalization: Label Propagation
Tweeque: Graph Partitioning for Spatio-Temporal Analysis
Experiments and Results
Future Work

4. Outline Introduction and Problem Statement Different Approaches
Social Graph Based: Our Approaches
Tweethood: Fuzzy k – Closest Friends with Variable Depth
Tweecalization: Label Propagation
Tweeque: Graph Partitioning for Spatio-Temporal Analysis
Experiments and Results
Future Work

5. Why is Location Important?
Privacy and Security
Trustworthiness
Location Driven Mining for Business
Location-Based Social Networking to generate US $21.14 billion by 20151
But only ~14.3% provide it explicitly2
1 According to New Report by Global Industry Analysts, Inc., (GIA) (
2 According to an experiment performed by us on 1 million users

6. Twitter - Basics Location # of Followers # of Following # of Tweets
Maximum 140 Characters

7. Why is location so important?

8. Privacy and Security Losing locational privacy forever
Users leave field blank, don’t want strangers to know their locations

9. Trustworthiness Trustworthiness is important in such cases
To be able to trust/verify the correctness of location mentioned in user profile
Corporate companies use social media for better advertising and marketing
Iran Elections of 2009
US State Department used Twitter as a source
Trustworthiness is important in such cases

10. Marketing and Business
Large corporations Walmart, Starbucks, United Airlines use social media
Great tool for inexpensive advertising
Getting feedback from users

11. The Problem Leave the location field blank in their Twitter profiles
Do not provide valid geographic information
“Justin Biebers heart”, “NON YA BISNESS!!”, “looking down on u people”
Provide incorrect locations which may actually exist in real world
“Nothing” in Arizona, “Little Heaven” in Connecticut
Provide several locations, difficult to identify the home location
“CALi b0Y $TuCC iN V3Ga$” – California boy stuck in Las Vegas, NV
(~35%) enter just country, state, county, etc. and no city level locations1
B. Hecht, L. Hong, B. Suh, E. H. Chi, “Tweets from justin biebers heart: the dynamics of the location field in user profiles”, In SIGCHI ’11.

12. Outline Introduction and Problem Statement Different Approaches
Social Graph Based: Our Approaches
Tweethood: Fuzzy k – Closest Friends with Variable Depth
Tweecalization: Label Propagation
Tweeque: Graph Partitioning for Spatio-Temporal Analysis
Experiments and Results
Future Work

13. Location Prediction in Social Networks
Two Approaches
Content Based1,2
Using Social Graph3,4,5
Z. Cheng, J. Caverlee, and K. Lee, “You are where you tweet: A content-based approach to geo-locating twitter users”. In CIKM ’10.
B. Hecht, L. Hong, B. Suh, E. H. Chi, “Tweets from justin biebers heart: the dynamics of the location field in user profiles”, In SIGCHI ’11.
S. Abrol, L. Khan and B. Thuraisingham,“Tweeque: Spatio-Temporal Analysis of Social Networks for Location Mining Using Graph Partitioning,” The First ASE/IEEE International Conference on Social Informatics, December 14-16, 2012, Washington D.C., USA.
S. Abrol., L. Khan and B. Thuraisingham “Tweecalization: Efficient and intelligent location mining in Twitter using semi-supervised learning,” 8th IEEE International Conference on Collaborative Computing, October 14–17, 2012 Pittsburgh, Pennsylvania.
S. Abrol., L. Khan, “Agglomerative clustering on fuzzy k-closest friends with variable depth for location mining,” The Second IEEE International Conference on Social Computing (SocialCom2010), Aug 20-22, 2010 Minneapolis, Minnesota.

14. Content Based Approach
Inaccurate – Location in Text not Location of User
Involves Ambiguity: Paris can mean
Paris Hilton
Paris, the capital of France
Paris, a town in Texas
Slow – Uses NLP/ Machine Learning techniques, searches gazetteers

15. Using Social Graphs
Based on Japanese Proverb - “When the character of a man is not clear to you, look at his friends.”
Relationship between geospatial proximity and friendship
Uses classical data mining algorithms for more accurate results
Faster and can be used for real world applications

16. Geospatial Proximity and Friendship
Form 1012 Twitter user pairs and identify geo distance
Curve follows power law, curve of form a(x+b)-c with exponent of -0.87

17. Graph Construction Vertices (data points) represents users
Edge represents ‘similarity’ between two users
Deal with special cases
Spammers – follow random people
Celebrities – followed by random people
Edge weight gets abbreviated

18. Defining Edge Weight Consists of two components: Trustworthiness (TW)
Mutual Friends (MF)

19. Trustworthiness
Fraction of friends which have the same label as the user himself
Intuition: A person who has stayed at the same place all his life will have most friends from same location and hence high trustworthiness
Location : Seattle/WA/USA A B C D E F G H I J
Location : Seattle/WA/USA
Location : Seattle/WA/USA
Trustworthiness: 0.6
Friend
Location:Seattle/WA/USA
Location : Seattle/WA/USA
Location : Seattle/WA/USA
Location : Seattle/WA/USA

20. Mutual Friends Chose number common friends for similarity
Better Accuracy
Low Time Complexity

21. Defining Edge Weight Defined as
Weightij=α×Max{TW(Ui), TW(Uj)} + (1- α) × MFij
0<α<1, typically chosen to be around 0.7

22. Outline Introduction and Problem Statement Different Approaches
Social Graph Based: Our Approaches
Tweethood: Fuzzy k – Closest Friends with Variable Depth
Tweecalization: Label Propagation
Tweeque: Graph Partitioning for Spatio-Temporal Analysis
Experiments and Results
Future Work

23. Tweethood: Fuzzy k-Closest Friends with Variable Depth
Choose k “closest” friends for the user
If location is not found look further for the answer
Each node is defined by a vector having locations with their respective probabilities
Boost and Aggregate at each step
Satyen Abrol, Latifur Khan, “TweetHood: Agglomerative Clustering on Fuzzy k-Closest Friends with Variable Depth for Location Mining”. In Proc. of the Second IEEE International Conference on Social Computing (SocialCom-2010), Minneapolis, USA, August 20-22, 2010

24. Agglomerative Clustering
Don’t want to find just any location
Want a location or group of locations with some confidence
Tradeoff between number of locations, distance between concepts, and total confidence
Construct matrix at each step with Objective Function of the above attributes.
Choose concepts with maximum values
Continue till we cross threshold

25. Find the location of John Doe

26. Social Network of John Doe
Friend 1
Friend 2
Friend 3
Friend n
CB1
CB2
CB3
CBn

27. Choose k closest friends of John Doe
Friend k
CB1
CB2
CB3
CBk

28. LOW ACCURACY Identify Locations Location : NULL
Friend 1
Friend 2
Friend 3
Friend k
Location : NULL
CB1
LOW ACCURACY
Location : Seattle, USA
CB2
CB3
Location : NULL
CBk
Location : NULL

29. What if we have depth=2 ? CB1 CB2 CB3 CBk Location : Seattle/WA/USAG H I J
Location : Seattle/WA/USA
Location : NULL
Location : NULL
Location : Dallas/TX/USA
Friend 1
Friend 2
Friend 3
Friend k
Location : NULL
Location : Sydney/AU
CB1
Location : Dallas/TX/USA
CB2
Location : NULL
Location : Richardson/TX/USA
CB3
Location : NULL
CBk

30. Location Vector for John Doe’s friends
Dallas/TX/USA 0.4
Seattle/WA/USA 0.2
Richardson/TX/USA 0.2
Sydney/AU 0.2
Friend 1
Friend 2
Friend 3
Friend k
CB1
Dallas/TX/USA 0.33
New Delhi/Delhi/India 0.33
Sunnyvale/CA/USA 0.33
CB2
CB3
Austin/TX/USA 0.50
Minneapolis/MN/USA 0.50
CBk
Plano/TX/USA 0.25
Boulder/CO/USA 0.25
Salt Lake City/UT/USA 0.25
London/London/GB 0.25

31. Location Vector for John Doe
Dallas/TX/USA
Seattle/WA/USA 0.05
Richardson/TX/USA 0.05
Sydney/AU 0.05
New Delhi/Delhi/IN
Sunnyvale/CA/USA
Austin/TX/USA
Minneapolis/MN/USA 0.125
Plano/TX/USA
Boulder/CO/USA
Salt Lake City/UT/US
London/GB

32. Agglomerative Clustering
John Doe
Dallas/TX/USA
Seattle/WA/USA 0.05
Richardson/TX/USA 0.05
Sydney/AU 0.05
New Delhi/Delhi/IN
Sunnyvale/CA/USA
Austin/TX/USA
Minneapolis/MN/USA 0.125
Plano/TX/USA
Boulder/CO/USA
Salt Lake City/UT/US
London/GB

33. Agglomerative Clustering
John Doe
{Dallas, Plano,
Richardson}/TX/USA
Seattle/WA/USA 0.05
Sydney/AU 0.05
New Delhi/Delhi/IN
Sunnyvale/CA/USA
Austin/TX/USA
Minneapolis/MN/USA 0.125
Boulder/CO/USA
Salt Lake City/UT/US
London/GB

34. Tweethood: Algorithm

35. Outline Introduction and Problem Statement Different Approaches
Social Graph Based: Our Approaches
Tweethood: Fuzzy k – Closest Friends with Variable Depth
Tweecalization: Label Propagation
Tweeque: Graph Partitioning for Spatio-Temporal Analysis
Experiments and Results
Future Work

36. Tweecalization: Label Propagation
But the availability of users with location is limited
Most of users do not have a location
Need a method that can learn from unlabeled data
Satyen Abrol, Latifur Khan and Bhavani Thuraisingham, “Tweecalization: Efficient and Intelligent location mining in Twitter using semi- supervised learning,” 8th IEEE International Conference on Collaborative Computing, October 14–17, 2012, Pittsburgh, Pennsylvania

37. Tweecalization: Label Propagation
Ideal scenario for semi supervised learning: Only a few friends with locations(labeled data)1
Use both labeled and unlabeled data for training
Points which are close to each other are more likely to share a label
Y. Bengio, O. Dellalleau, and N. L. Roux, “Label propagation and quadratic criterion,” In O. Chapelle, B. Schlkopf and A. Zien (Eds.), Semi-supervised learning. MIT Press, 2006.

38. Label Propagation: An Illustration
“CLAMPED LOCATIONS”
Central User
Friends with location
Friends without location ?

39. Tweecalization: Algorithm

40. Outline Introduction and Problem Statement Different Approaches
Social Graph Based: Our Approaches
Tweethood: Fuzzy k – Closest Friends with Variable Depth
Tweecalization: Label Propagation
Tweeque: Graph Partitioning for Spatio-Temporal Analysis
Experiments and Results
Future Work

41. What About Temporal Analysis?
None of the existing works do temporal analysis
What about migration/ geographical mobility?

42. Migration/Geographical Mobility
4% to 6% every year, means 12 to 17 million each year
United States Census Bureau - Geographical Mobility/Migration Data -

43. Migration/Geographical Mobility
Migration as a function of age
People aged have a higher probability to move
High Migration Rate: College and Jobs
Low Migration Rate: Old age, people settle down
United States Census Bureau - Geographical Mobility/Migration Data -

44. Facebook Users and Mobility
Let us look at the cumulative effect
Only 28% to 37% are currently living in their hometown
Based on our experiments on 300k Public Facebook Profiles

45. Twitter Users and Mobility
Linking Twitter users to migration
33% of all Twitter users are aged years
Based on our findings by [1] ABI Research. Online. Available:

46. Tweeque: Graph Partitioning
How do we know if “this” is the current location for a user?
How do we perform temporal analysis of friendships?
Propose a technique that indirectly infers the current location
Satyen Abrol, Latifur Khan and Bhavani Thuraisingham,“Tweeque: Spatio-Temporal Analysis of Social Networks for Location Mining Using Graph Partitioning,” The First ASE/IEEE International Conference on Social Informatics, December 14-16, 2012, Washington D.C., USA.

47. Observation 1: Social Cliques and Location
Our definition: A social clique is an inclusive group of people that share friendship
Apart from friendship, what is the attribute that links members of a clique? Individual Locations
All members of a clique were or are at a particular geographical location at a particular instant of time like college, school, a company, etc.

48. Observation 2: Migration and Time
As shown previously over course of time, people have tendency to migrate
Based on these two observations we hypothesize
If we can divide the social graph of a particular user into cliques and check for location based purity of the cliques, we can accurately separate out his current location from previous locations.
Migration is our latent time factor

49. Friends from high school in Dallas Friends from college in Boston
Tweeque: An example
Friend 1
Friend 2
Friend 3
Friend 4
Friend 5
Friend 6
Friend 7
Friend n
Friends from high school in Dallas
Friends from college in Boston
Relatives/Cousins
Friends from job in Seattle

50. Tweeque: An example
All Friends of the User

51. Tweeque: An example Social Clique #1 (High School)
Social Clique #2 (College)
Social Clique #3 (Current Work)
Social Clique #4 (Relatives)

52. Tweeque: An Example High School College Relatives Work Dallas/TX/USA
Boston/MA/USA
Singapore
Seattle/WA/USA
Seattle/WA/USA
Portland/OR/USA
Sydney/Australia
Seattle/WA/USA
Dallas/TX/USA
Austin/TX/USA
Dallas/TX/USA
Dallas/TX/USA
San Diego/CA/USA
Boston/MA/USA
Dallas/TX/USA
Seattle/WA/USA
New York/NY/USA
Dallas/TX/USA
Ontario/Canada
Redmond/WA/USA
Purity (Dallas) = 0.32
Purity (Boston) = 0.45
Purity (Dallas) = 0.18
Purity (Seattle) = 0.69

53. Tweeque: Graph Partitioning

54. Tweeque: Graph Partitioning
J. Shi and J. Malik, “Normalized Cuts and Image Segmentation,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 8, pp , Aug

55. Tweeque: Graph Partitioning

56. Tweeque: Algorithm

57. Tweeque: Purity Voting

58. Outline Introduction and Problem Statement Different Approaches
Social Graph Based: Our Approaches
Tweethood: Fuzzy k – Closest Friends with Variable Depth
Tweecalization: Label Propagation
Tweeque: Graph Partitioning for Spatio-Temporal Analysis
Experiments and Results
Future Work

59. Experiment Data
Randomly choose 1000 Twitter users

60. Experiments and Results
75.5% for city level prediction
80.1% for country level prediction
We observe that the accuracy saturates after depth 4
Six degrees of separation is the idea that everyone is on average approximately six steps away, by way of introduction, from any other person in the world`
For Twitter this distance is found to be 4.67

61. Comparison of Different Approaches
Tweethood1
Tweecalization2
Tweeque3
Content Based4
Accuracy (City)
72.1%
75.5%
76.3%
35.6% - 51%
Accuracy (Country)
80.1%
84.9%
52.3%
Complexity
O(n)
O(n3)
N/A
Temporal Analysis No
Yes
Satyen Abrol, Latifur Khan, “TweetHood: Agglomerative Clustering on Fuzzy k-Closest Friends with Variable Depth for Location Mining”. In Proc. of the Second IEEE International Conference on Social Computing (SocialCom-2010), Minneapolis, USA, August 20-22, 2010 (Nominated for best paper award, Acceptance Rate:13%)
Satyen Abrol, Latifur Khan and Bhavani Thuraisingham, “Tweecalization: Efficient and Intelligent location mining in Twitter using semi- supervised learning,” 8th IEEE International Conference on Collaborative Computing, October 14–17, 2012, Pittsburgh, Pennsylvania
Satyen Abrol, Latifur Khan and Bhavani Thuraisingham,“Tweeque: Spatio-Temporal Analysis of Social Networks for Location Mining Using Graph Partitioning,” The First ASE/IEEE International Conference on Social Informatics, December 14-16, 2012, Washington D.C., USA.
Z. Cheng, J. Caverlee, and K. Lee, “You are where you tweet: A content-based approach to geo-locating twitter users”. In CIKM ’10.

62. Outline Introduction and Problem Statement Different Approaches
Social Graph Based: Our Approaches
Tweethood: Fuzzy k – Closest Friends with Variable Depth
Tweecalization: Label Propagation
Tweeque: Graph Partitioning for Spatio-Temporal Analysis
Experiments and Results
Future Work

63. Contributions
Developed three graph based location mining algorithms for online social networks
Maps location mining problem to k-nearest neighbor, semi supervised and graph partitioning problem
Outperform content based approach in time and accuracy
Relationship between geospatial proximity and friendship
Effect of geographical mobility on current location of users

64. Future Work Combining Content and Graph based methods
Score based geo-tagging technique1
Associating keywords with locations to build probabilistic model: “cowboys”  Dallas, “casino”  Las Vegas
Since tweets have timestamps, it leads to more accurate prediction of current location
1 Satyen Abrol, Latifur Khan, Tahseen Al-khateeb, “MapIt: Smarter Searches using Location Driven Knowledge Discovery and Mining”, In Proc. of 1st SIGSPATIAL ACM GIS 2009 International Workshop on Querying and Mining Uncertain Spatio-Temporal Data (QUeST), Nov 2009, Seattle.

65. Future Work
Improve scalability of current algorithms using cloud computing framework
Each of the friends of a user is handled by a separate node in the distributed environment
Micro-level location identification
Identify specific points of interests (POIs) such as restaurants, place of work, etc from tweets
Identify comfort zone for a user
Use Foursquare check-in dataset: over 30 million POIs all over the world

66. Publications
Satyen Abrol, Latifur Khan and Bhavani Thuraisingham,“Tweeque: Spatio-Temporal Analysis of Social Networks for Location Mining Using Graph Partitioning,” The First ASE/IEEE International Conference on Social Informatics, December 14-16, 2012, Washington D.C., USA.
Satyen Abrol, Latifur Khan and Bhavani Thuraisingham, “Tweecalization: Efficient and Intelligent location mining in Twitter using semi- supervised learning,” 8th IEEE International Conference on Collaborative Computing, October 14–17, 2012, Pittsburgh, Pennsylvania
Satyen Abrol, Latifur Khan, “TweetHood: Agglomerative Clustering on Fuzzy k-Closest Friends with Variable Depth for Location Mining”. In Proc. of the Second IEEE International Conference on Social Computing (SocialCom-2010), Minneapolis, USA, August 20-22, 2010 (Nominated for best paper award, Acceptance Rate:13%)

67. Publications
Satyen Abrol And Latifur Khan, “TWinner: Understanding News Queries With Geo-Content Using Twitter”. In Proc. of 6th Workshop on Geographic Information Retrieval (GIR'10) At Zurich, Switzerland.
Satyen Abrol, Latifur Khan, Tahseen Al-khateeb, “MapIt: Smarter Searches using Location Driven Knowledge Discovery and Mining”, In Proc. of 1st SIGSPATIAL ACM GIS 2009 International Workshop on Querying and Mining Uncertain Spatio-Temporal Data (QUeST), Nov 2009, Seattle.
Satyen Abrol, Latifur Khan, Vaibhav Khadilkar, Bhavani M. Thuraisingham, Tyrone Cadenhead, “Design and implementation of SNODSOC: Novel class detection for social network analysis”, ISI 2012:

68. Thank You!
Questions?