Towards Social User Profiling: Unified and Discriminative Influence Model for Inferring Home Locations

Towards Social User Profiling: Unified and Discriminative Influence Model for Inferring Home Locations

Rui Li, Shengjie Wang, Hongbo Deng, Rui Wang, Kevin Chen-Chuan Chang University of Illinois at Urbana-Champaign

13/04/24 KDE Seminar: Yuto Yamaguchi 1

Paper Introduction Speaker: Yuto Yamaguchi

KDD ‘12

Introduction • Users’ locations are important to many applications

•  e.g.) Advertisement, Recommendation

• But most of users do not provide their location information •  On Twitter, only 16% of users register city level locations in their

profiles

•  The objective of this paper is to profile users’ home locations in social network.


General Ideas for Location Inference • A user more likely to follow another user who lives near

•  e.g.) A user in Chicago follows another user in Chicago

•  [Backstorm et al., WWW ‘10], •  [Clodoveu et al., T-GIS ‘11] , …

• A user more likely to post about a near location to him •  e.g.) A user in Houston posts about rockets

•  [Cheng et al., CIKM ‘10], •  [Chandra et al., SocialCom ’11], •  [Kinsella et al., SMUC ‘11], …


Challenges • On Twitter, following network and tweets provide valuable

signals for profiling their home locations

• But there are two challenges,

• Scarce Signals •  126 friends on average, but only 16% of them provide locations •  6 location related terms in every 100 tweets

• Noisy Signals •  a user may follow another user who lives in a distant location •  a user may post about distant locations


Ideas in this paper •  The authors propose a unified discriminative influence

model UDI which has two features below

• Unified Signals (for scarce signal challenge) •  Integrates social network and user-centric data (i.e., tweets) in a

probabilistic framework, which is viewed as a heterogeneous graph

• Discriminative Influence (for noisy signal challenge) •  Users and locations have their own influence scope

e.g.) Lady Gaga (with a broad influence scope) is more likely to be followed by a user far away

à users with broad scopes do not provide so strong signals for location inference


Contributions • Propose a unified discriminative influence model UDI

•  Heterogeneous graph •  Influence scope

• Propose two location profiling methods using the above model (introduced later) •  Local prediction method •  Global prediction method

• Conduct extensive experiments using Twitter dataset •  Their method can place 66% users within 100 miles error distance


PROBLEM FORMULATION


Heterogeneous Graph


User nodes ui ∈Uvj ∈VVenue nodes

If ui posts about vj, create an edge <ui, vj>

If ui follows uj, create an edge <ui, uj>

Location Profiling Problem


Given a Twitter Graph G, estimate a location for each user ui so as to make close to ui’s true location

L̂ uiL̂ ui

L ui

INFLUENCE MODEL


Motivation 1/2


Near users (venues) are more likely to be followed (tweeted) by other users

Motivation 2/2


Each user (venue) has an influence scope of different size

Influential user

regular user

Basic Ideas for the Influence model • Geographically influential user has a broad influence

scope •  e.g.) world wide celebrities such as Lady Gaga

•  The fact that a user follows a geographically influential user does NOT provide valuable signals for location inference

•  e.g.) NOT VALUABLE: a user follows Lady Gaga VALUABLE: a user follows a regular user in Chicago


Model Formulation •  The authors adopt a Gaussian distribution to model the

above characteristics


latitude longitude

probability to follow (tweet)

N(Lni ,Σni)

node ni’s influence scope

Influence scope – users


latitude longitude

probability to follow

N(Lui ,Σui)

user ui’s influence scope

High probability to follow ui Low probability

to follow ui

user ui’s home location

Influence scope – venues


latitude longitude

probability to tweet

N(Lvi ,Σvi)

venue vi’s influence scope

High probability to tweet Low probability

to tweet

venue vi’s location

Different scope size – users


high influence

Regular user Geographically influential user

More likely to be followed by distant users

Different scope size – venues


high influence

Regular venue Geographically influential venue

More likely to be tweeted by distant users

Model Parameters • Mean and variance for each Gaussian

• Mean is the location of node ni

• Variance decides the size of each influence scope

•  The number of parameters is


N(Lni ,Σni)

Lni

Σni

Σni=

σ ni0

0 σ ni

"

#

$$

%

&

''

2 U + V( )

LOCATION PROFILING METHODS Local prediction method Global prediction method


Basic Ideas for Location Profiling


Estimate such model parameters that maximize the likelihood of obtaining the given Twitter graph

Lni Σniand for each node ni Parameters:

Local Prediction Method •  This method only considers the ego-network

•  Maximize the likelihood of this network


tweet

follow

labeled user

labeled user labeled user

unlabeled user

labeled user: his location is known

unlabeled user: his location is unknown

ego-network

Likelihood Function of Local Method


P ego-network of ui | parameters( ) =

P uj follows ui | Luj ,Lui ,Σui( )uj∈Followers ui( )∏ ×

P ui follows uj | Lui ,Luj ,Σuj( )uj∈Followees ui( )∏ ×

P ui tweets v j | Lui ,Lvj ,Σvj( )vj∈Venues ui( )∏

These are Gaussian

Maximize this function

Each Gaussian


P uj follows ui | Luj ,Lui ,Σui( ) =

12πσ ui

2 expXui

− Xuj( )2+ Yui −Yuj( )

2

−2σ ui2

#

$

%%%

&

'

(((

•  High probability if ui and uj is close •  High probability if ui has broad influence scope

Solution of Local Method


Xui=

Xuj

σ uiuj∈ followers ui( )∑ +

Xuj

σ ujuj∈ followees ui( )∑ +

Xvj

σ viv j∈venues ui( )∑

1σ uiuj∈ followers ui( )

∑ +1σ ujuj∈ followees ui( )

∑ +1σ viv j∈venues ui( )

∑

σ ui2 =

Xui− Xuj( )

2+ Yui −Yuj( )

2

2 followers ui( )uj∈ followers ui( )∑

Obtained as closed-form (no need to memorize)

substitute

Global Prediction Method •  This method maximizes the likelihood of the whole network

•  Predict locations of unknown users simultaneously


Likelihood Function of Global Method


P whole network | parameters( ) =

P ui follows uj | Lui ,Luj ,Σuj( )ui ,uj ∈FollowEdges∏ ×

P ui tweets v j | Lui ,Lvj ,Σvj( )ui ,vj ∈TweetEdges∏

These are Gaussian

Maximize this function

Iterative Algorithm for Global Method • Global method has no closed form solution

à Iterative algorithm


1. Initialize locations for all unlabeled users 2.  3. repeat

1. update for all nodes using 2. repeat

1. update for all unlabeled users using 3. until converge 4.  5. 

4. until converge

Lu

σ nk

Luk

Lu ← Luk

k←1

σ nk

Lu

Luk

Luk← k +1

EXPERIMENTS


Dataset •  Twitter dataset

•  Crawled Profiles, followers, and followees of 3,980,061 users •  Geocoded their location profiles into coordinates based on U.S.

Gazetteer •  630,187 users are correctly geocoded ß labeled users

•  158,220 of labeled users have at least one labeled neighbor •  neighbor: follower or followee

•  Crawled at most 600 tweets for each labeled user, and obtained 139,180 users’ tweets •  Other users are protected users

•  Using this dataset, the authors conducted five-fold cross validation •  80% of 139,180 users are for training set, 20% are for test set •  Repeat 5 runs


Methods • Compared 6 methods

•  BaseU: Backstorm et al.’s method [1] •  Using only social graph

•  BaseC: Cheng et al.’s method [2] •  Using only tweets

•  UDIU: Local prediction method, but only uses user nodes •  UDIC: Local prediction method, but only uses venue nodes •  UDII: Local prediction method •  UDIG: Global prediction method


No influence model

[1] Backstorm et al., “Find me if you can: improving geographical prediction with social and spatial proximity”, WWW’10 [2] Cheng et al., “You are where you tweet: a content-based approach to geo-locating twitter users”, CIKM’10

Results – Prediction results


ACC: Ratio of correctly predicted users within 100 miles AED@k%: Average error distance of top k% users

•  Influence model is effective to predict locations •  Comparing BaseU and UDIU (BaseC and UDIC)

•  Integrating both signals is effective to predict locations •  Comparing UDIU and UDII (UDIC and UDII)

•  Global method improves Local one only 1.5% •  Comparing UDIG and UDII

Results – Global and Local


+9% in ACC 20% training users and 80% test users

In the case that most of users are unlabeled, the global method improves the local one substantially

Results – Influence scope


•  Users with a large number of followers do not always have large σ •  e.g.) MythBusters Official have larger σ than Lady

Gaga but have smaller number of followers

CONCLUSION


Conclusion • Proposed

•  Unified discriminative influence model (UDI) •  Two location prediction method based on influence model

•  global and local

• Conducted experiments using large Twitter dataset •  Proposed methods significantly outperform existing methods

• NO future work


Towards Social User Profiling: Unified and Discriminative Influence Model for Inferring Home Locations

Documents

user ui

user venue

social user profiling

yuto yamaguchikdd

near location

users home locations

usercentric data

location inference