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Friendship Paradox Redux, ICWSM 2013

Presentation of "Friendship Paradox Redux: Your Friends are More Interesting Than You Are" for ICWSM 2013
by

Nathan Hodas

on 5 December 2013

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Transcript of Friendship Paradox Redux, ICWSM 2013

Activity Paradox
Feld, American Journal of Sociology 1991
Confirm and Explore the Friendship Paradox on Twitter
Demonstrate New Paradoxes: Activity Paradox and Virality Paradox
Implications: Information Overload and Altered Propagation

Flux of Incoming Information Grows Faster Than User Engagement
Your Friends and Followers...
have more Friends and Followers,
are more Active,
and send and receive more Viral content than you.
"Your friends are more interesting than you are!"
Friendship Paradox Redux:
Your Friends are More Interesting Than You

Friendship Paradoxes on Twitter
Friendship Paradoxes on Twitter:
Does Directionality Matter?
The Friendship Paradox holds in Every Direction!
New Paradoxes
Friendship Paradox
The Friendship Paradox⟩
Related Work
Our Contribution
Paradoxes and
Information Overload

Is information overlaod inevitable?
Consequence of Information Overload
Conclusions
friendship paradox
In "meatspace" (Feld 1991; Zuckerman and Jost 2001)
Facebook (Ugander et al. 2011)
Twitter* (Garcia-Herranz et al. 2012)
friends as detectors
Virtual Outbreaks (Garcia-Herranz et al. 2012)
Pathogenic Outbreaks (Christakis and Fowler 2010)
social perception and decision making
Alcohol and Drug Abuse (Tucker et al. 2011; Wolfson 2000)
Wealth (Morselli and Tremblay 2004; Amuendo-Dorantes 2007)
Extraversion (Pollet et al. 2011; Querca et al. 2012)
Friend = Followee
Does the Friendship Paradox hold on Twitter in all directions?
"friends are better connected"
"followers are better connected"
"followers are more popular"
"friends are more popular"
shaded region corresponds to paradox condition
"Your friends are more active than you are."
Virality Paradox
"Your friends send and receive higher virality content than you do."
Ratio of Cascade Sizes Sent
Ratio of Cascade Sizes Received
Incoming Tweets Scale Super-Linearly!
Paradox worsens with connectivity
Finding "Overloaded Users"
Bin users into activity levels:
<5 Tweets over 2-month window
5-19 Tweets
20-59 Tweets
>= 60 Tweets
Top 33% of users in each category, based on received tweets, we call "Overloaded"
Bottom 33% of users in each category, based on received tweets, we call "Underloaded"
Mean of average size of received URL cascades
Dilemma: Popularity vs. Overload
Most Active Users
Least Active Users
Incoming information scales super-linearly with the number of friends
Overloaded Users receive a more popular content than Underloaded Users, on average
Implications
Researchers
Users are not uniformly sampling interesting content
Unexplained systematic biases stem from connectivity-activity correlations
Practitioners
Increasing engagement with social media exacerbates observed paradoxes.
Decreasing engagement decreases exposure to popular content
Challenge: build compelling social network w/o information overload
Nathan Hodas
Farshad Kooti
Kristina Lerman
Information Sciences Insitute
University of Southern California

Many Thanks
Co-Authors
Farshad Kooti
Prof. Kristina Lerman
Funders
This material is based upon work supported in part by the Air Force Office of Scientific Research under Contract Nos. FA9550-10-1-0569, by the National Science Foundation under Grant No. CIF-1217605, and by DARPA under Contract No. W911NF-12-1-0034.
Avg. Posted Tweets Per Friend / Posted Tweets by User
476 Million Tweets (Random Sample of Firehose) from June to December 2009 (Yang and Leskovec 2011)
+
Follower Graph of 40 Million Users as of Summer 2009 (Kwak et al. 2010)
5.8 Million Users w/ ~200 Million links
3.4 Million Users sending 37 Million Tweets
Users active over a two month window
Correlation between Number of Friends and Activity
Overloaded users SEND and RECEIVE more popular content
*One direction only
99.6%
98%
99%
98%
88% or 99%
86%
76%
Full transcript