How Does the Data Sampling Strategy Impact the Discovery of Information

Diffusion in Social Media?

Munmun De Choudhury1, Yu-Ru Lin1, Hari Sundaram1,K. Selcuk Candan1, Lexing Xie2, Aisling Kelliher1

1Arizona State University, Tempe, AZ2IBM T. J. Watson Research Center, Hawthorne, NY

This talk is about sampling the social

web

Background and MotivationProblem DefinitionSampling Diffusion DataEvaluation of Diffusion SamplesExperimental StudyConclusions

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Modern Social Interactional Modes

FacebookSlashdot

Engadget

Flickr

LiveJournalDigg

YouTubeBlogger

MetaFilterReddit

MySpaceOrkut

Twitter

We are attracted to social media, in part due

to large scale datasets

Viral Marketing, Advertizing Campaigns

Collaboration, “Wisdom of the Crowds”

Crisis management w.r.t. real-time events

Is there something more fundamental happening here than just scale?

Information Diffusion

140 characters can cause revolutions

Inference is based on data quality

( )y f x=

What has been done?

Snowball

Random walk

Forest Fire[Leskovec et. al, KDD 2005]

Designed to capture topology

But not context or content!

Problem DefinitionSampling Diffusion DataEvaluation of Diffusion SamplesExperimental StudyConclusions

Background and Motivation

Two simple questions

What is the role of context in the sampling process?

What fraction of the social data should we sample?

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Twitter• “Tweets”: 140 character length shared content.

– RT (or re-tweet feature), hashtags (e.g. #iranelection), bit.ly encoded URLs

• Follower / Following relationship.• “Trending topics” e.g. #musicmonday, #formulaone.

• Diffusion via (1) RT feature, (2) shared URL (e.g. bit.ly, tinyurl), (3) same hashtag

(1) RT based diffusion

(2) URL based diffusion

(3) hashtag based diffusion

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Diffusion Series

Social graph

Diffusion series

Sampling Diffusion DataEvaluation of Diffusion SamplesExperimental StudyConclusions

Background and MotivationProblem Definition

What are our sampling strategies?

Assume we are give N, the number of nodes to pick

And θ, the topic

Plus the social graph G

What if we ignored topology?

We can also sample topology using Forest Fire

Evaluation of Diffusion SamplesExperimental StudyConclusions

Background and MotivationProblem DefinitionSampling Diffusion Data

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Diffusion Saturation Metrics• User-based (Volume, participation, dissemination)• Topology-based (Reach, spread, cascade instances, collection size)• Time-based (Rate)

Volume

ParticipationDissemination

Reach

Spread

Cascade Instances

Collection Size

Rate

Distortion

Fθ (m;S) =

mθ −mθ (S)

mθ

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1−D(EN+1D (θ),E

N+1S (θ)) where, E

N+1S (θ) is the search volume, and

EN+1D (θ)= | l

m(S

N+1(θ))| /Q

Dm≤N+1∑ ,

| lm(S

N+1(θ))| is the number of nodes at slot l

m in the collection S

N+1(θ), and

QD= | l

m(S

N+1(θ))|.

m∑ Similarly,

Diffusion Response Metrics

• Search and News Trends, i.e. ability of the social graph sample to correlate with external temporal variables like user search behavior and news items featured online (http://news.google.com/), given as:

1 1 11 ( ( ), ( )) where, ( ) is the news volume.DN N ND E E E θ θ θ+ + +

Experimental StudyConclusions

Background and MotivationProblem DefinitionSampling Diffusion DataEvaluation of Diffusion Samples

Reference Set

500 seed users using mashable.com

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Experimental Setup• ~465K users, ~836K edges (“follower” / “following” relationships) and 29.5M tweets.• 125 randomly chosen “trending topics” from Twitter, between Oct and Nov 2009.• Trending topic – theme association based on OpenCalais (http://www.opencalais.com/).

Themes Trending topics

Politics Obama, Senate, Afghanistan, Tehran, Healthcare

Entertainment_Culture Beyonce, Eagles, Michael Jackson, #britney3premiere

Sports Chargers, Cliff Lee, Dodgers, Formula One, New York Yankees

Technology_Internet Android 2, Bing, Google Wave, Windows 7, #Firefox5

Social Issues Swine Flu, Unemployment, #BeatCancer, #Stoptheviolence

• Dataset released for non-commercial research purposes: http://www.public.asu.edu/~mdechoud/temp/released-data/

R1

Is this a slam dunk for forest fire + activity?

Looking at themes tells a more nuanced story

What is a “good” sampling ratio?

What happens when ρ = 0.3?

Conclusions

Background and MotivationProblem DefinitionSampling Diffusion DataEvaluation of Diffusion SamplesExperimental Study

Social networks are causing significant changes in our lives

Inferences about social phenomena is affected by data quality

Topic + topology + seed attribute makes a difference to sampling

Thanks!

munmun@asu.eduFor Publications / Datasets: http://www.public.asu.edu/~mdechoud/Twitter: @munmun10

Questions?