Exploring Big Data in Social Networks

virgilio@dcc.ufmg.br

(meira@dcc.ufmg.br)

INWEB – National Science and Technology Institute for Web

Federal University of Minas Gerais - UFMG

May 2013

Some thoughts about computing, future and

innovation…

What happens in 60 seconds on the Internet?

4

Explosion of

Web Data

5

• BIG DATA: • data collection, • storage, • management, • automated large-scale analysis

Research interests

BIG DATA Algorithms and

MACHINE LEARNING

SOCIAL and ECONOMICS

• characterization • models • incentives • privacy • network effects • crowdsourcing • anti-social behavior • spam and malware s

• algorithms around social networks • VERY large graphs • data mining • analytics

• Systems • Infrastructure • cloud • characterization

The fundamental challenge of Big Data

is not collecting data -- it's making

sense of it.

1) What is the starting point?

2) What are the computation paths to discovery?

3) What are the appropriate algorithms?

3) How to visualize the findings?

Experimental Methodology

Measure Analyze Model

Synthesize

Models

An

aly

sis

Validation Observations Artifacts

Algorithms

Distributions of Random Variables

Synthetic Workloads

Logs and Traces

What if questions:

Challenges in Online Social

Networking Research

• Explosive growth in size, complexity, and unstructured

data;

• Enabled by various experimental methods: observational

studies, simulations,..., huge amount of data;

• It is “big data,” the vast sets of information gathered

by researchers at companies like Facebook, Google and

Microsoft from patterns of cellphone calls, text

messages and Internet clicks by millions of users

around the world. Companies often refuse to make such

information public, sometimes for competitive reasons

and sometimes to protect customers’ privacy. (New York

Times, May 21)

Enablers of Big Data

Hardware capability Applications & Algorithms

Storage capacity Online social networking

Network bandwidth Algorithmic breakthroughs: machine learning and data mining

Exponentially increasing capability at constant cost

Cloud: Cost reductions and scalability improvements in computation

Processing capacity Sensors everywhere

Price of 1 gigabyte of storage over time

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Year Cost

1981 $300,000

1987 $50,000

1990 $10,000

1994 $1000

1997 $100

2000 $10

2004 $1

2012 $0.10

OSN Research Focus

1.Understand: characteristics of social

graphs of real data;

2.Discover: properties of social graphs;

3.Engineer: social graph built.

OSN research approach

• Computational sociology:

A natural sciences approach

– Gather and analyze OSN data to study problems

in sociology

• Social computing: An engineering approach

– Build systems that support / leverage human

social interactions

– Understand human behavior (as opposed of

considering it annoying noise)

• Inspired by sociological theories

The Atlantic

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Understanding Factors that Affect

Response Rates in Twitter(*)

• Active users can receive ∼1000 tweets per day;

• Approximately 36% of all tweets worth

reading, 39% are neutral and 25% are “junk”;

• Interesting Questions

– Do Twitter users receive more information than they

are able to consume?

– Is it possible to identify factors that affect

interactions (replies and retweets)?

(*) ACM Hypertext 2012, joint work with Giovanni Comarela, Mark Crovella, F. Benevenuto

Datasets: big data

• Collected in August/September 2009, it

contains the following information:

• Users: 54,981,152 Tweets: 1,755,925,520

(almost a complete history) Social Graph:

1,963,263,821 social links

• It contains information related to Replies

and Retweets (interactions)

Characterization

• Waiting Times (overload evidence)

– How long does a tweet wait in the timeline to

be replied (retweeted)?

• Factors that affect interactions

– Message Age

– Previous Interactions

– Sending Rate

Waiting Times

Message Age

Previous interaction

• Are previously replied (retweeted) users more

likely to be replied (retweeted) again?

• We computed for each user i the conditional

probability that a message m will be replied

(retweeted) by i given that i has replied

(retweeted) the sender of m before;

Sending rate

• Are users with a higher sending rate more

likely to be replied (retweeted)?

• For each user i, for each j ∈ Outi we compared the sending rate of j with the

fraction of her tweets replied (retweeted) by

i.

Reorganizing the Twitter

Timeline

• Use the knowledge presented in order to create a

new way to show tweets for the users

• More interesting tweets (more likely to be replied

or retweeted) in the top of the timeline.

• Two schemes

– Naive Bayes (NB)

– Support Vector Machine (SVM)

– Three attributes

• Age(m): Age of m

• SR(m): Sending rate of the sender of m

• I(m): Binary indicator for previous

interactions with the sender of m

Results

Google+

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New Kid on the Block: Exploring the Google+ Social Graph, ACM Internet Measurement Conference, Sigcomm, 2012, Boston Joint work with: G. Magno, G. Comarela, D. Saez and Meeyong Cha.

Online Social Networks

• OSNs now reach 82% of the world’s

Internet-using population (1.2

billion)

• Social Networking accounts for 19%

of all time spent online

Social Networking is the most popular online activity worldwide

Source: comScore, December 21, 2011 27

Google+ Growth

28 Google+ is the fastest growing OSN

Days

# users

Goal: characterization

• Analyze how much and what kind of personal

information people share in Google+

• Measure statistics of the Google+ social

graph and compare with other OSNs

• Evaluate the impact of geography on user

behavior in Google+

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Dataset: big data

• Nov. 11th Dec.

27th (2011)

• 27,556,390 profiles

• 35,114,957 nodes

• 575,141,097 edges

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What kind of information do

people share more?

Privacy Concerns

• Users revealing more information on their

profiles have greater risk in privacy

• In Facebook (young users, to friends)¹:

– 64.1% share e-mail

– 10.7% share telephone

– 10.7% share home address

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What kind of information do

people share more?

• In Google+ (public):

– 0.22% share Work contact

– 0.21% share Home contact

– 0.26% share telephone numbers (72,736

users)

• Users that shared telephone: tel-users

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Number of fields shared in

profile

34 Tel-users share more information

Information shared by users

Women are less likely to share

phone number

The majority of tel-users are single; a smaller

fraction of them are in a relationship.

Fraction of Indian users in the tel-users group is twice as big as in other countries

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How are people connected on

Google+?

Structural Characteristics

of Social Graphs

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New network Lower number

of friends

Higher reciprocity =

More social

“Hidden” edges Higher avg. path length

Diameter similar to Twitter, lower than Facebook

Structural Characteristics –

Clust. Coef.

Higher Clustering Coefficient than Twitter 38

What is the impact of geography

on the social relationships?

Geo-location Information

• Question: is the

geographical

location of users an

important factor in

the formation of

social links?

• Extract GPS coordinates

from map image

• Retrieve country

information

• 6,621,644 users with

valid country inf.

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Patterns Across Geo-locations –

Average Path Miles

58% of friends were separated by less than a thousand miles

Physical distance has influence on the intensity of the relationship

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Social Links Across Geography are users in the same country more likely to be

friends than users in different countries

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US is dominant on the influx of edges

Populous countries have more self-loops

G+ Observations

• Google+ is more social than Twitter

– Higher reciprocity

– Higher clustering coefficient

– Reflects offline relationship

• Users exhibit different notions and

expectations in Google+, based on

geography

– Privacy

– Content

– Connections

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Concluding Remarks

• Big data has created new opportunities for

scientific discoveries in the realm of

social computing:

– user preference understanding

– data mining

– summarization and aggregation

– explorative analysis of large data sets

– privacy

– scalable services