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 and Champaign

User profiling infers users’ essential attributes and is important for many services.

and many others.

Personalized Search

Targeted Advertisement

Search Engines

Advertisers

Richard

User

Job: StudentLocation:

Champaign

This paper aims to profile Twitter users’ home locations from both Tweets and Following Network

User Centric Data (Tweets)

Social Network Data (Following network)

Jessie

Rob Lady Gaga

Cindy

Richard

TechChruch

Input

Profiling a User’s Home LocationLocation: Champaign

OutputA user’s home location is defined as the place most his activities happen. It is different from a real-time geo position (e.g., Starbucks at green street)

In Context of Twitter Network

The problem is difficult due to scarce signal challenge

Only 6% messages contains location related terms!

JessieChampaign

Rob Lady GagaNew York

Cindy

Richard

TechChruchUnknown

Only 16% users have locations on their profiles!

Unknown

Following Network

San Francisco

Tweets

The problem is difficult due to noisy signal challenge

Tweets

JessieChampaign

Rob Lady GagaNew York

Cindy

Richard

TechChruchUnknown

Unknown

San Francisco

Following Network

A user tweets about locations different from his home location.

User follows friends who live different locations from his home location.

Scarce Signal Challenge

We propose a unified and discriminative probabilistic framework.

Noisy Signal Challenge

Unify two types of resources as a twitter graph

Model the likelihood of an edge between two nodes via a discriminative Influence model

Profile locations via maximizing the likelihood of observing the graph.

We unify two types of resources as a Directed Heterogeneous Graph

We unify two types of resources as nodes on a heterogeneous graph

We model it as a directed graph.

We associate locations to the nodes.

We aim to infer the locations of unlabeled nodes with locations of labeled nodes.

Head Node

Tail Node

New York

?Champaign

Beijing

San Francisco

?

?

Champaignv2

v1

u2

U6

u1

u3

u4

u5

Unlabeled Node

labeled Node

We observe two key characteristics for the probability of an edge between two nodes

Observation 1 The probability decreases as their distance increases

Observation 2 At the same distance, different head (Chicago, Champaign) nodes have different probabilities to attract tail nodes.

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latitude

Spread of Word "Champaign"

longitude

coun

t

How likely a tail node nj at L(nj) builds an edge e<ni, nj> a head node ni at L(ni)

Conceptual level Discriminative Influence Model θni Influence probabilities decrease from the

center. Different nodes have different influence scope.

We propose a discriminative influence model to capture the two key characteristics

Mathematical Level Gaussian Model

2

in

2

ujiu

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ujiu

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i

2π

)y(y)x(x

ninij e

2π

1))L(n,θ|n,nP(e

A local profiling algorithm profiles the location of a user via the edges from and to his labeled neighbors.

simple but efficient closed-from solution.

?

Champaignv2

v1

u2

u1

u4

u5

Champaign

New York

Beijing

San Francisco

Influence Scope

Average Distance of a User’ s Followers

User LocationWeighted Average of Different Resources

A global algorithm profiles all the users’ locations together via all the edges in the graph.

complex but accurate iterative algorithm.

New York

?Champaign

Beijing

San Francisco

?

?

Champaignv2

v1

u2

U6

u1

u3

u4

u5

The local algorithm only uses limited information.

Our global algorithm aims to use all information.

We incorporate additional knowledge as constraints for maximizing the likelihood function.

Additional Knowledge: e.g., users only live in cities or towns

Constraint Optimization: we maximize the likelihood in each method under constraints.

Data Set: We crawled a subset of Twitter. We used the users having locations on

profiles. There are 139K users, 50 million tweets and

2 million following relationships. Methods:

User-based Location Profiling Content-based Location Profiling

We compare our method with the-state-of-arts methods on a large Twitter corpus.

Our algorithms are better than the baseline methods as we model edges discriminatively.

Our algorithms can take advantages of modeling two different types of resources

The global profiling algorithm can further improve the local profiling algorithm.

We explore both social network and user-centric data for profiling users locations in a unified approach.

We introduce a discriminative influence model.

We develop two effective profiling methods and extend the methods via modeling

constraints. The framework could be further extended to

profiling other attributes.

Conclusion and Future work

Questions?