Download - Location Mining from Online Social Networks

Location Mining from Online Social Networks

Satyen AbrolAdvisors:

Dr. Latifur KhanDr. Bhavani Thuraisingham

Location Mining in Online Social Networks

What is the city level home location of a user?

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

Outline




Analysis

• Experiments and Results• Future Work

Twitter - Basics

Tweets:Maximum 140 Characters

# of Tweets

# of Following

# of FollowersLocation

Why is location so important?

Privacy and Security

• Losing locational privacy forever Users leave field blank, don’t want

strangers to know their locations

• http://pleaserobme.com/

Trustworthiness

• 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

To be able to trust/verify the correctness of location mentioned in user profile

Marketing and Business

• Large corporations Walmart, Starbucks, United Airlines use social media Great tool for inexpensive advertising Getting feedback from users

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

1. 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.

Outline




Analysis


Location Prediction in Social Networks

• Two Approaches– Content Based1,2

– Using Social Graph3,4,5

1. Z. Cheng, J. Caverlee, and K. Lee, “You are where you tweet: A content-based approach to geo-locating twitter users”. In CIKM ’10.2. 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. 3. 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.4. 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.5. 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.

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

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

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

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

Defining Edge Weight

• Consists of two components:– Trustworthiness (TW)– Mutual Friends (MF)

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






Friend

Trustw

orthiness:

0.6

Location:Seattle/WA/USA

Mutual Friends

• Chose number common friends for similarity– Better Accuracy– Low Time Complexity

• Defined as

Weightij=α×Max{TW(Ui), TW(Uj)} + (1- α) × MFij

• 0<α<1, typically chosen to be around 0.7

Defining Edge Weight

Outline




Analysis


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 stepSatyen 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

Find the location of John Doe

Social Network of John Doe

CB1

CB2

CB3

CBn

Choose k closest friends of John Doe

CB1

CB2

CB3

CBk

Identify Locations

CB1

CB2

CB3

CBk

Location : NULL

Location : NULL

Location : NULL

Location : Seattle, USA

LO

W

AC

CU

RA

CY

What if we have depth=2 ?

CB1

CB2

CB3

CBk


Location : NULL

Location : NULL

Location : Sydney/AU

Location : Dallas/TX/USA

Location : Richardson/TX/USA

Location : NULL

Location : NULL

Location : Dallas/TX/USA

Location : NULL

CB1

CB2

CB3

CBk

Dallas/TX/USA 0.4Seattle/WA/USA 0.2Richardson/TX/USA 0.2Sydney/AU 0.2

Dallas/TX/USA 0.33New Delhi/Delhi/India 0.33Sunnyvale/CA/USA 0.33

Austin/TX/USA 0.50Minneapolis/MN/USA 0.50

Plano/TX/USA 0.25Boulder/CO/USA 0.25Salt Lake City/UT/USA 0.25London/London/GB 0.25

Location Vector for John Doe’s friends

Location Vector for John Doe

Dallas/TX/USA 0.1825Seattle/WA/USA 0.05Richardson/TX/USA0.05Sydney/AU 0.05New Delhi/Delhi/IN 0.0825Sunnyvale/CA/USA 0.0825Austin/TX/USA 0.125Minneapolis/MN/USA 0.125Plano/TX/USA 0.0625Boulder/CO/USA 0.0625Salt Lake City/UT/US 0.0625London/GB 0.0625

Agglomerative Clustering

Dallas/TX/USA 0.1825Seattle/WA/USA 0.05Richardson/TX/USA0.05Sydney/AU 0.05New Delhi/Delhi/IN 0.0825Sunnyvale/CA/USA 0.0825Austin/TX/USA 0.125Minneapolis/MN/USA 0.125Plano/TX/USA 0.0625Boulder/CO/USA 0.0625Salt Lake City/UT/US 0.0625London/GB 0.0625

{Dallas, Plano, Richardson}/TX/USA 0.295

Seattle/WA/USA 0.05Sydney/AU 0.05New Delhi/Delhi/IN 0.0825Sunnyvale/CA/USA 0.0825Austin/TX/USA 0.125Minneapolis/MN/USA0.125Boulder/CO/USA 0.0625Salt Lake City/UT/US 0.0625London/GB 0.0625

Agglomerative Clustering

Tweethood: Algorithm

Outline


Tweethood: Fuzzy k – Closest Friends with Variable Depth Tweecalization: Label Propagation Tweeque: Graph Partitioning for Spatio-Temporal Analysis


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

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

1. 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.

Label Propagation: An Illustration

?

Central User

Friends with location

Friends without location

“CLAMPED LOCATIONS”

Tweecalization: Algorithm

Outline


Tweethood: Fuzzy k – Closest Friends with Variable Depth Tweecalization: Label Propagation Tweeque: Graph Partitioning for Spatio-Temporal

Analysis


What About Temporal Analysis?

• None of the existing works do temporal analysis

• What about migration/ geographical mobility?

Migration/Geographical Mobility

• 4% to 6% every year, means 12 to 17 million each year

1. United States Census Bureau - Geographical Mobility/Migration Data - http://www.census.gov/hhes/migration/

Migration/Geographical Mobility

• Migration as a function of age

• People aged 20-29 have a higher probability to move

High Migration Rate: College and Jobs

Low Migration Rate: Old age, people settle down

1. United States Census Bureau - Geographical Mobility/Migration Data - http://www.census.gov/hhes/migration/

Facebook Users and Mobility

• Let us look at the cumulative effect

• Only 28% to 37% are currently living in their hometown

1. Based on our experiments on 300k Public Facebook Profiles

Twitter Users and Mobility

• Linking Twitter users to migration

• 33% of all Twitter users are aged 25-34 years

1. Based on our findings by [1] ABI Research. Online. Available: http://www.abiresearch.com

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.

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.

• 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

Observation 2: Migration and Time

Tweeque: An exampleFriends from high school in Dallas

Friends from college in Boston

Relatives/Cousins

Friends from job in Seattle

Tweeque: An example

All Friends of the User

Tweeque: An exampleSocial Clique #1 (High School)

Social Clique #2 (College)

Social Clique #3 (Current Work)

Social Clique #4 (Relatives)

Tweeque: An Example

Dallas/TX/USA

Seattle/WA/USA

Dallas/TX/USA

San Diego/CA/USA

New York/NY/USA

Boston/MA/USA

Portland/OR/USA

Austin/TX/USA

Boston/MA/USA

Dallas/TX/USA

Singapore

Sydney/Australia

Dallas/TX/USA

Dallas/TX/USA

Ontario/Canada

Seattle/WA/USA

Seattle/WA/USA

Dallas/TX/USA

Seattle/WA/USA

Redmond/WA/USA

High School College Relatives Work

Purity (Dallas) = 0.32 Purity (Boston) = 0.45 Purity (Dallas) = 0.18 Purity (Seattle) = 0.69


J. Shi and J. Malik, “Normalized Cuts and Image Segmentation,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 8, pp. 888-905, Aug. 2000.

Tweeque: Algorithm

Tweeque: Purity Voting

Outline




Experiment Data

• Randomly choose 1000 Twitter users

Experiments and Results

• 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

Comparison of Different Approaches

Tweethood1 Tweecalization2 Tweeque3 Content Based4

Accuracy (City) 72.1% 75.5% 76.3% 35.6% - 51%

Accuracy (Country)

80.1% 80.1% 84.9% 52.3%

Complexity O(n) O(n3) O(n3) N/A

Temporal Analysis

No No Yes Yes

1. 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%)

2. 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

3. 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.

4. Z. Cheng, J. Caverlee, and K. Lee, “You are where you tweet: A content-based approach to geo-locating twitter users”. In CIKM ’10.

Outline




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

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.

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

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%)

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: 215-220

Thank You!

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Download - Location Mining from Online Social Networks

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