![Page 1: Landmark-Based User Location Inference in Social Media YUTO YAMAGUCHI †, TOSHIYUKI AMAGASA † AND HIROYUKI KITAGAWA † †UNIVERSITY OF TSUKUBA 13/10/08 COSN](https://reader036.vdocument.in/reader036/viewer/2022062620/551a9f31550346e0158b5818/html5/thumbnails/1.jpg)
Landmark-Based User Location Inferencein Social Media
YUTO YAMAGUCHI†, TOSHIYUKI AMAGASA †
AND HIROYUKI KITAGAWA †
†UNIVERSITY OF TSUKUBA
13/10/08
COSN 2013 - Yuto Yamaguchi 1
![Page 2: Landmark-Based User Location Inference in Social Media YUTO YAMAGUCHI †, TOSHIYUKI AMAGASA † AND HIROYUKI KITAGAWA † †UNIVERSITY OF TSUKUBA 13/10/08 COSN](https://reader036.vdocument.in/reader036/viewer/2022062620/551a9f31550346e0158b5818/html5/thumbnails/2.jpg)
LOCATION-RELATED INFORMATION
13/10/08
COSN 2013 - Yuto Yamaguchi 2
Eating seafood !!!
I’m at Logan airport
Profile
Residence: Tokyo, Japan
COSN @ northeastern
![Page 3: Landmark-Based User Location Inference in Social Media YUTO YAMAGUCHI †, TOSHIYUKI AMAGASA † AND HIROYUKI KITAGAWA † †UNIVERSITY OF TSUKUBA 13/10/08 COSN](https://reader036.vdocument.in/reader036/viewer/2022062620/551a9f31550346e0158b5818/html5/thumbnails/3.jpg)
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
13/10/08
COSN 2013 - Yuto Yamaguchi 3
![Page 4: Landmark-Based User Location Inference in Social Media YUTO YAMAGUCHI †, TOSHIYUKI AMAGASA † AND HIROYUKI KITAGAWA † †UNIVERSITY OF TSUKUBA 13/10/08 COSN](https://reader036.vdocument.in/reader036/viewer/2022062620/551a9f31550346e0158b5818/html5/thumbnails/4.jpg)
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
13/10/08
COSN 2013 - Yuto Yamaguchi 4
![Page 5: Landmark-Based User Location Inference in Social Media YUTO YAMAGUCHI †, TOSHIYUKI AMAGASA † AND HIROYUKI KITAGAWA † †UNIVERSITY OF TSUKUBA 13/10/08 COSN](https://reader036.vdocument.in/reader036/viewer/2022062620/551a9f31550346e0158b5818/html5/thumbnails/5.jpg)
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]
13/10/08
COSN 2013 - Yuto Yamaguchi 5
Our focus
![Page 6: Landmark-Based User Location Inference in Social Media YUTO YAMAGUCHI †, TOSHIYUKI AMAGASA † AND HIROYUKI KITAGAWA † †UNIVERSITY OF TSUKUBA 13/10/08 COSN](https://reader036.vdocument.in/reader036/viewer/2022062620/551a9f31550346e0158b5818/html5/thumbnails/6.jpg)
GRAPH-BASED APPROACH (1/2)
Basic Idea
13/10/08
COSN 2013 - Yuto Yamaguchi 6
Boston
Boston
Boston Chicago
New York Boston?
friends
![Page 7: Landmark-Based User Location Inference in Social Media YUTO YAMAGUCHI †, TOSHIYUKI AMAGASA † AND HIROYUKI KITAGAWA † †UNIVERSITY OF TSUKUBA 13/10/08 COSN](https://reader036.vdocument.in/reader036/viewer/2022062620/551a9f31550346e0158b5818/html5/thumbnails/7.jpg)
GRAPH-BASED APPROACH (2/2)
Closeness Assumption
13/10/08
COSN 2013 - Yuto Yamaguchi 7
Friends
Not friends
Spatially close
Spatially distant
Really close?
60% are 100km distant
![Page 8: Landmark-Based User Location Inference in Social Media YUTO YAMAGUCHI †, TOSHIYUKI AMAGASA † AND HIROYUKI KITAGAWA † †UNIVERSITY OF TSUKUBA 13/10/08 COSN](https://reader036.vdocument.in/reader036/viewer/2022062620/551a9f31550346e0158b5818/html5/thumbnails/8.jpg)
CONCENTRATION ASSUMPTION
13/10/08
COSN 2013 - Yuto Yamaguchi 8
Boston
Boston?
LANDMARK
Unknown
NYChicago
![Page 9: Landmark-Based User Location Inference in Social Media YUTO YAMAGUCHI †, TOSHIYUKI AMAGASA † AND HIROYUKI KITAGAWA † †UNIVERSITY OF TSUKUBA 13/10/08 COSN](https://reader036.vdocument.in/reader036/viewer/2022062620/551a9f31550346e0158b5818/html5/thumbnails/9.jpg)
LANDMARKS 13/10/08
9COSN 2013 - Yuto Yamaguchi
![Page 10: Landmark-Based User Location Inference in Social Media YUTO YAMAGUCHI †, TOSHIYUKI AMAGASA † AND HIROYUKI KITAGAWA † †UNIVERSITY OF TSUKUBA 13/10/08 COSN](https://reader036.vdocument.in/reader036/viewer/2022062620/551a9f31550346e0158b5818/html5/thumbnails/10.jpg)
REQUIREMENTS Small Dispersion
Large Centrality
13/10/08
COSN 2013 - Yuto Yamaguchi 10
![Page 11: Landmark-Based User Location Inference in Social Media YUTO YAMAGUCHI †, TOSHIYUKI AMAGASA † AND HIROYUKI KITAGAWA † †UNIVERSITY OF TSUKUBA 13/10/08 COSN](https://reader036.vdocument.in/reader036/viewer/2022062620/551a9f31550346e0158b5818/html5/thumbnails/11.jpg)
EXAMPLES IN TWITTER
13/10/08
COSN 2013 - Yuto Yamaguchi 11
![Page 12: Landmark-Based User Location Inference in Social Media YUTO YAMAGUCHI †, TOSHIYUKI AMAGASA † AND HIROYUKI KITAGAWA † †UNIVERSITY OF TSUKUBA 13/10/08 COSN](https://reader036.vdocument.in/reader036/viewer/2022062620/551a9f31550346e0158b5818/html5/thumbnails/12.jpg)
LANDMARKS MAPPING
13/10/08
COSN 2013 - Yuto Yamaguchi 12
Red: all usersBlue: landmarks
![Page 13: Landmark-Based User Location Inference in Social Media YUTO YAMAGUCHI †, TOSHIYUKI AMAGASA † AND HIROYUKI KITAGAWA † †UNIVERSITY OF TSUKUBA 13/10/08 COSN](https://reader036.vdocument.in/reader036/viewer/2022062620/551a9f31550346e0158b5818/html5/thumbnails/13.jpg)
PROPOSED METHOD 13/10/08
13COSN 2013 - Yuto Yamaguchi
![Page 14: Landmark-Based User Location Inference in Social Media YUTO YAMAGUCHI †, TOSHIYUKI AMAGASA † AND HIROYUKI KITAGAWA † †UNIVERSITY OF TSUKUBA 13/10/08 COSN](https://reader036.vdocument.in/reader036/viewer/2022062620/551a9f31550346e0158b5818/html5/thumbnails/14.jpg)
OVERVIEW
Probabilistic Model
Modeling
13/10/08
COSN 2013 - Yuto Yamaguchi 14
Each user has his/her location distribution
Location inference = Selecting the location with the largest probability density
location set
LANDMARK MIXTURE MODEL
![Page 15: Landmark-Based User Location Inference in Social Media YUTO YAMAGUCHI †, TOSHIYUKI AMAGASA † AND HIROYUKI KITAGAWA † †UNIVERSITY OF TSUKUBA 13/10/08 COSN](https://reader036.vdocument.in/reader036/viewer/2022062620/551a9f31550346e0158b5818/html5/thumbnails/15.jpg)
DOMINANCE DISTRIBUTION
Spatial distribution of followers’ home locations
Modeled as Gaussian
Landmarks have small covariances
many followers at the center
13/10/08
COSN 2013 - Yuto Yamaguchi 15
latitude
longitude
manyfollowers
fewfollowers
![Page 16: Landmark-Based User Location Inference in Social Media YUTO YAMAGUCHI †, TOSHIYUKI AMAGASA † AND HIROYUKI KITAGAWA † †UNIVERSITY OF TSUKUBA 13/10/08 COSN](https://reader036.vdocument.in/reader036/viewer/2022062620/551a9f31550346e0158b5818/html5/thumbnails/16.jpg)
LANDMARK MIXTURE MODEL (LMM)
13/10/08
COSN 2013 - Yuto Yamaguchi 16
Inferencetarget user
follow
Landmark
Non-landmark
Non-landmark
Dominancedistribution
Mixtureweight
Large weight for landmark
![Page 17: Landmark-Based User Location Inference in Social Media YUTO YAMAGUCHI †, TOSHIYUKI AMAGASA † AND HIROYUKI KITAGAWA † †UNIVERSITY OF TSUKUBA 13/10/08 COSN](https://reader036.vdocument.in/reader036/viewer/2022062620/551a9f31550346e0158b5818/html5/thumbnails/17.jpg)
MIXTURE WEIGHTS
13/10/08
COSN 2013 - Yuto Yamaguchi 17
Proportional to centrality
Landmark Non-landmark
Large mixture weight Small mixture weight
![Page 18: Landmark-Based User Location Inference in Social Media YUTO YAMAGUCHI †, TOSHIYUKI AMAGASA † AND HIROYUKI KITAGAWA † †UNIVERSITY OF TSUKUBA 13/10/08 COSN](https://reader036.vdocument.in/reader036/viewer/2022062620/551a9f31550346e0158b5818/html5/thumbnails/18.jpg)
CONFIDENCE CONSTRAINT
If the distribution does not have a clear peak,
we should not infer the location of that user
13/10/08
COSN 2013 - Yuto Yamaguchi 18
High precision but low recall
![Page 19: Landmark-Based User Location Inference in Social Media YUTO YAMAGUCHI †, TOSHIYUKI AMAGASA † AND HIROYUKI KITAGAWA † †UNIVERSITY OF TSUKUBA 13/10/08 COSN](https://reader036.vdocument.in/reader036/viewer/2022062620/551a9f31550346e0158b5818/html5/thumbnails/19.jpg)
CENTRALITY CONSTRAINT
We can reduce the cost by ignoring non-landmarks
13/10/08
COSN 2013 - Yuto Yamaguchi 19
low cost but low recall
Inferencetarget user
follow
Landmark
Non-landmark
Non-landmark
![Page 20: Landmark-Based User Location Inference in Social Media YUTO YAMAGUCHI †, TOSHIYUKI AMAGASA † AND HIROYUKI KITAGAWA † †UNIVERSITY OF TSUKUBA 13/10/08 COSN](https://reader036.vdocument.in/reader036/viewer/2022062620/551a9f31550346e0158b5818/html5/thumbnails/20.jpg)
EXPERIMENTS 13/10/08
20COSN 2013 - Yuto Yamaguchi
![Page 21: Landmark-Based User Location Inference in Social Media YUTO YAMAGUCHI †, TOSHIYUKI AMAGASA † AND HIROYUKI KITAGAWA † †UNIVERSITY OF TSUKUBA 13/10/08 COSN](https://reader036.vdocument.in/reader036/viewer/2022062620/551a9f31550346e0158b5818/html5/thumbnails/21.jpg)
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)
13/10/08
COSN 2013 - Yuto Yamaguchi 21
![Page 22: Landmark-Based User Location Inference in Social Media YUTO YAMAGUCHI †, TOSHIYUKI AMAGASA † AND HIROYUKI KITAGAWA † †UNIVERSITY OF TSUKUBA 13/10/08 COSN](https://reader036.vdocument.in/reader036/viewer/2022062620/551a9f31550346e0158b5818/html5/thumbnails/22.jpg)
PERFORMANCE COMPARISON
13/10/08
COSN 2013 - Yuto Yamaguchi 22
Compared three methods LMM: our method UDI: [Li+, KDD’12] Naïve: Spatial median
![Page 23: Landmark-Based User Location Inference in Social Media YUTO YAMAGUCHI †, TOSHIYUKI AMAGASA † AND HIROYUKI KITAGAWA † †UNIVERSITY OF TSUKUBA 13/10/08 COSN](https://reader036.vdocument.in/reader036/viewer/2022062620/551a9f31550346e0158b5818/html5/thumbnails/23.jpg)
EFFECT OF CONFIDENCE CONSTRAINT
13/10/08
COSN 2013 - Yuto Yamaguchi 23
p0
We can adjust the trade-off between precision and recall
![Page 24: Landmark-Based User Location Inference in Social Media YUTO YAMAGUCHI †, TOSHIYUKI AMAGASA † AND HIROYUKI KITAGAWA † †UNIVERSITY OF TSUKUBA 13/10/08 COSN](https://reader036.vdocument.in/reader036/viewer/2022062620/551a9f31550346e0158b5818/html5/thumbnails/24.jpg)
EFFECT OF CENTRALITY CONSTRAINT
13/10/08
COSN 2013 - Yuto Yamaguchi 24
c0 We can adjust the trade-off between cost and recall
![Page 25: Landmark-Based User Location Inference in Social Media YUTO YAMAGUCHI †, TOSHIYUKI AMAGASA † AND HIROYUKI KITAGAWA † †UNIVERSITY OF TSUKUBA 13/10/08 COSN](https://reader036.vdocument.in/reader036/viewer/2022062620/551a9f31550346e0158b5818/html5/thumbnails/25.jpg)
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
COSN 2013 - Yuto Yamaguchi 25