Online Actions with Offline Impact: !How Online Social Networks Influence Online and Offline User Behavior

Tim Althoff, Pranav Jindal, Jure Leskovec @timalthoff

Alice Bob

Tim Althoff, Stanford University

Alice Bob

Tim Althoff, Stanford University

What happens to Alice & Bob?

Alice Bob

Tim Althoff, Stanford University

friend

Previous Research: Online Behaviors

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Online Behavior

Tim Althoff, Stanford University

§  Homophily – user similarity §  Triadic closure – common friends §  Community structure §  Network growth §  …

Want to understand online à offline!

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This talk  

§  Homophily – user similarity §  Triadic closure – common friends §  Community structure §  Network growth §  …

Online Behavior

Offline Behavior

Tim Althoff, Stanford University

?  

§  Physical activity §  Diet §  Smoking §  Political mobilization §  …

What causes change in behavior?

5 Tim Althoff, Stanford University

Alice

’s Ac

tivity

Time

What causes change in behavior?

5 Tim Althoff, Stanford University

Alice

’s Ac

tivity

Time

What causes change in behavior?

5 Tim Althoff, Stanford University

Alice

’s Ac

tivity

Time

Why !increase?

What causes change in behavior?

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Social Influence

Tim Althoff, Stanford University

Alice

’s Ac

tivity

Time

Why !increase?

What causes change in behavior?

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Social Influence

Intrinsic Motivation

Tim Althoff, Stanford University

Alice

’s Ac

tivity

Time

Why !increase?

Main Research Question §  Do online social networks influence

offline (real-world) behavior? §  Challenge: How to disentangle social

influence from intrinsic motivation? §  Never observe alternate universe where

the friendship did not happen

6 Tim Althoff, Stanford University

Our Contributions 1.  New method for causal identification of

social influence §  A natural experiment exploiting delay of

friendship acceptance

2.  Social networking leads to a 5 months !long increase of up to ~350 daily steps

3.  Can predict which users will be most

influenced by new social network connections with 79% accuracy

7 Tim Althoff, Stanford University

Activity Tracking Dataset §  Azumio’s Argus activity

tracking app (iOS/Android)

§  6 million users §  211k users join social network §  Enables interaction & notifications

§  791 million actions §  online (posts) §  offline actions (physical activity; !

accelerometer-based) §  5 year timespan

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Disentangling Motivation & Influence

9 Tim Althoff, Stanford University

Disentangling Motivation & Influence

9 Tim Althoff, Stanford University

Disentangling Motivation & Influence

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Direct accept Delayed accept

Tim Althoff, Stanford University

Disentangling Motivation & Influence

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Direct accept Delayed accept

Tim Althoff, Stanford University

Disentangling Motivation & Influence

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Direct accept Delayed accept

Difference isolates social influence

influence

motivation motivation

influence

Tim Althoff, Stanford University

A natural experiment for Alice? §  Is assignment to “direct accept” (<1 day)

and “delayed accept” (>7 days) !as if random?

à  Groups would be indistinguishable! or balanced

§  Balancing criterion: Covariates within standardized mean difference (SMD) of 0.25 SD [Stuart, 2010]

10 Tim Althoff, Stanford University

Testing Alternative Hypotheses Maybe direct accepter’s are… §  …more active?

§  Avg. daily steps 7 days before §  0.07 SD < 0.25 SD à No

§  …closer friends? §  # common friends at request §  0.08 SD < 0.25 SD à No

§  …newer to social network? §  # days since joining §  0.179 SD < 0.25 SD à No

+19 further hypotheses in paper; all balanced

11 Tim Althoff, Stanford University

Testing Alternative Hypotheses Maybe direct accepter’s are… §  …more active?

§  Avg. daily steps 7 days before §  0.07 SD < 0.25 SD à No

§  …closer friends? §  # common friends at request §  0.08 SD < 0.25 SD à No

§  …newer to social network? §  # days since joining §  0.179 SD < 0.25 SD à No

+19 further hypotheses in paper; all balanced

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“True” natural experiment!

Tim Althoff, Stanford University

Experimental Results

Delayed acceptedrequest

Acceptedrequest

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350

400St

eps

diffe

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} }}M

M + I

I

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45%

55%

Experimental Results

Delayed acceptedrequest

Acceptedrequest

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400St

eps

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} }}M

M + I

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12 Tim Althoff, Stanford University

45%

55%

Experimental Results

Delayed acceptedrequest

Acceptedrequest

0

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100

150

200

250

300

350

400St

eps

diffe

renc

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} }}M

M + I

I

12 Tim Althoff, Stanford University

45%

55%

Causal effect of increasing !

real-world activity !due to online social network

by ~350 steps/day !

Effect of Social Network Over Time

­4 ­3 ­2 ­1 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 207ime in weeks

5600

5800

6000

6200

6400

6600

Average daily steps 7reatment

Control

  !

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§  Longitudinal study with Difference-in-Differences design and matched control group

Effect of Social Network Over Time

­4 ­3 ­2 ­1 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 207ime in weeks

5600

5800

6000

6200

6400

6600

Average daily steps 7reatment

Control

§  Online social networks significantly increase offline physical activity for ~5 months

13 Tim Althoff, Stanford University

§  Longitudinal study with Difference-in-Differences design and matched control group

Heterogeneous Effects §  Robust: Several additional experiments all

reveal similar effects §  Joining network also affects online behavior

§  App usage (increased 30%) §  One-year retention (increased 17%)

       

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Heterogeneous Effects §  Robust: Several additional experiments all

reveal similar effects §  Joining network also affects online behavior

§  App usage (increased 30%) §  One-year retention (increased 17%)

§  Repeatability: 1st friend > 2nd friend §  Tie Strength: friend > follower §  Initiator: sender > receiver §  Demographics: > (52% more!)

14 Tim Althoff, Stanford University

Heterogeneous Effects §  Robust: Several additional experiments all

reveal similar effects §  Joining network also affects online behavior

§  App usage (increased 30%) §  One-year retention (increased 17%)

§  Repeatability: 1st friend > 2nd friend §  Tie Strength: friend > follower §  Initiator: sender > receiver §  Demographics: > (52% more!)

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Can use these insights to predict behavior change!

Predicting Behavior Change §  Which individuals will be most influenced

by new social network connections? §  Binary prediction task: Will you increase

your activity over next 7 days? (55.4% do) §  Short-term behavior change

§  Dataset: 432k new friendships §  Gradient Boosted Tree models

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Predicting Behavior Change §  Which individuals will be most influenced

by new social network connections? §  Binary prediction task: Will you increase

your activity over next 7 days? (55.4% do) §  Short-term behavior change

§  Dataset: 432k new friendships §  Gradient Boosted Tree models

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Can predict positive behavior change with 79% ROC AUC

Summary & Conclusions Findings §  Social networks influence behavior – online & offline §  Can predict which users will be most influenced by

new social network connections

Implications §  How to design & maintain social networks as

effective support communities that lead to healthier lifestyles? §  Group composition §  Friendship recommendation algorithms §  Enabling supportive and encouraging interactions

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Acknowledgements §  We thank Azumio for donating data from their Argus

app for independent research.

@timalthoff althoff@cs.stanford.edu www.timalthoff.com

Ask me anything!

Tim Althoff, Stanford University 17

Travel Award