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Landmark-Based User Location Inferencein Social Media
YUTO YAMAGUCHI†, TOSHIYUKI AMAGASA †
AND HIROYUKI KITAGAWA †
†UNIVERSITY OF TSUKUBA
13/10/08
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LOCATION-RELATED INFORMATION
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Eating seafood !!!
I’m at Logan airport
Profile
Residence: Tokyo, Japan
COSN @ northeastern
APPLICATIONS
Various Researches using Home Locations
Outbreak Modeling [Poul+, ICWSM’12]
Real-World Event Detection [Sakaki+, WWW’12]
Analyzing Disasters [Mandel+, LSM’12]
Other Useful Applications
Location-aware Recommender [Levandoski+, ICDE’12]
Merketing, Ads
Disaster Warning
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OUR PROBLEM
Location profiles are not available for …
76% of Twitter users [Cheng et al., CIKM’10]
94% of Facebook users [Backstrom et al., WWW’10]
This reduces opportunities of location information
User Home Location Inference
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USER HOME LOCATION INFERENCE Content-Based Approaches
[Cheng et al., CIKM’10] [Kinsella et al., SMUC’11] [Chandra et al., SocialCom’11]
Graph-Based Approaches
[Backstrom et al., WWW’10] [Sadilek et al., WSDM’12] [Jurgens, ICWSM’13]
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Our focus
GRAPH-BASED APPROACH (1/2)
Basic Idea
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Boston
Boston
Boston Chicago
New York Boston?
friends
GRAPH-BASED APPROACH (2/2)
Closeness Assumption
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Friends
Not friends
Spatially close
Spatially distant
Really close?
60% are 100km distant
CONCENTRATION ASSUMPTION
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Boston
Boston?
LANDMARK
Unknown
NYChicago
LANDMARKS 13/10/08
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REQUIREMENTS Small Dispersion
Large Centrality
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EXAMPLES IN TWITTER
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LANDMARKS MAPPING
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Red: all usersBlue: landmarks
PROPOSED METHOD 13/10/08
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OVERVIEW
Probabilistic Model
Modeling
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Each user has his/her location distribution
Location inference = Selecting the location with the largest probability density
location set
LANDMARK MIXTURE MODEL
DOMINANCE DISTRIBUTION
Spatial distribution of followers’ home locations
Modeled as Gaussian
Landmarks have small covariances
many followers at the center
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latitude
longitude
manyfollowers
fewfollowers
LANDMARK MIXTURE MODEL (LMM)
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Inferencetarget user
follow
Landmark
Non-landmark
Non-landmark
Dominancedistribution
Mixtureweight
Large weight for landmark
MIXTURE WEIGHTS
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Proportional to centrality
Landmark Non-landmark
Large mixture weight Small mixture weight
CONFIDENCE CONSTRAINT
If the distribution does not have a clear peak,
we should not infer the location of that user
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High precision but low recall
CENTRALITY CONSTRAINT
We can reduce the cost by ignoring non-landmarks
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low cost but low recall
Inferencetarget user
follow
Landmark
Non-landmark
Non-landmark
EXPERIMENTS 13/10/08
20COSN 2013 - Yuto Yamaguchi
DATASET
Twitter dataset provided by [Li et al., KDD’12]
3M users in the U.S.
285M follow edges
Geocode their location profiles for ground truth
465K users (15%) labeled users
Test set
46K users (10% of labeled users)
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PERFORMANCE COMPARISON
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Compared three methods LMM: our method UDI: [Li+, KDD’12] Naïve: Spatial median
EFFECT OF CONFIDENCE CONSTRAINT
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p0
We can adjust the trade-off between precision and recall
EFFECT OF CENTRALITY CONSTRAINT
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c0 We can adjust the trade-off between cost and recall
CONCLUSIONIntroduced the concentration assumptioninstead of widely-used closeness assumption
There exist landmarks
Proposed landmark mixture model
Outperforms the state-of-the-art method
Confidence / Centrality constraint
Future work
Other application of landmarks
Recommending landmarks or their tweets 13/10/08
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