landmark-based user location inference in social media
DESCRIPTION
Landmark-Based User Location Inference in Social Media. Yuto Yamaguchi † , Toshiyuki Amagasa † and Hiroyuki Kitagawa † †University of Tsukuba. location-related information. Profile. Residence: Tokyo, Japan. Eating seafood !!! . I’m at Logan airport . COSN @ northeastern . - PowerPoint PPT PresentationTRANSCRIPT
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Landmark-Based User Location Inferencein Social MediaYUTO YAMAGUCHI†, TOSHIYUKI AMAGASA †
AND HIROYUKI KITAGAWA†
†UNIVERSITY OF TSUKUBA
13/10/08
COSN 2013 - Yuto Yamaguchi 1
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LOCATION-RELATED INFORMATION
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Eating seafood !!!
I’m at Logan airport
Profile
Residence: Tokyo, Japan
COSN @ northeastern
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APPLICATIONSVarious 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 PROBLEMLocation 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
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GRAPH-BASED APPROACH (1/2)Basic Idea
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Boston
Boston
Boston Chicago
New York Boston?
friends
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GRAPH-BASED APPROACH (2/2)Closeness Assumption
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Friends
Not friends
Spatially close
Spatially distant
Really close?
60% are 100km distant
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CONCENTRATION ASSUMPTION
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Boston
Boston?
LANDMARK
Unknown
NYChicago
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LANDMARKS 13/10/08 9COSN 2013 - Yuto Yamaguchi
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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
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PROPOSED METHOD 13/10/08 13COSN 2013 - Yuto Yamaguchi
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OVERVIEWProbabilistic Model
Modeling
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Each user has his/her location distribution
Location inference = Selecting the location with the largest probability densitylocation set
LANDMARK MIXTURE MODEL
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DOMINANCE DISTRIBUTIONSpatial 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
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LANDMARK MIXTURE MODEL (LMM)
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Inferencetarget user
follow
Landmark
Non-landmark
Non-landmark
Dominancedistribution
Mixtureweight
Large weight for landmark
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MIXTURE WEIGHTS
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Proportional to centrality
Landmark Non-landmark
Large mixture weight Small mixture weight
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CONFIDENCE CONSTRAINTIf 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
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CENTRALITY CONSTRAINTWe 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
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EXPERIMENTS 13/10/08 20COSN 2013 - Yuto Yamaguchi
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DATASETTwitter 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
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EFFECT OF CONFIDENCE CONSTRAINT
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p0
We can adjust the trade-off between precision and recall
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EFFECT OF CENTRALITY CONSTRAINT
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c0 We can adjust the trade-off between cost and recall
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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
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