Human Behavior as Recorded on the Web WebST Symposium Thursday, February 24 th, 2011 Imperial Palace...
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Transcript of Human Behavior as Recorded on the Web WebST Symposium Thursday, February 24 th, 2011 Imperial Palace...
Human Behavior as Recorded on the Web
WebST SymposiumThursday, February 24th, 2011Imperial Palace Hotel, Seoul
Sue Moon
Graduate Program of Web Science and Technology &Department of Computer Science
KAIST
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Historic Records of Human Activities
함부라비 법전 ?
알타미라 동굴 그림
훈민정음 해례
3
Personal Correspondents
4
Come Internet
• Your explicit trace of existence– Emails– Chat room activities– Messenger activities– Files you create/modify/delete– Newsgroup– Comments– Web
• Your implicit trace– Search keyword logs
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MyLifeBits
Picture of LifeBits (MSR Mountain View guy)
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In the Middle East
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Information Diffusion
• Reflects “potentials of power transition”• Egypt, Libya, MENA ( 뭐의 약자 ?)• Twitter/FB critical or supplementary?– One thing for sure: records of word-of-mouth
spreading
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Traces of Twitpic
• Guaranteed single source• Unique URL• Twitter-internal starting point
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Some Twitter specifics
• Our unit of information = Twitpic– Affiliated web site of pictures
• Why not tweets themselves?– We are looking at tweets of trending topics
• Why not general URLs?– Typically in shortened forms (bit.ly, tinyurl, t.co)– Can be in multiple shortened forms– Hard to identify sources
Duration of Twitpic Spreading
# of Tweets
median duration
(day)
Which Twitpic is most popular?
• # of tweeted– Form of recommendation (quality)
• # of viewed– User clicks on URL (popularity)
• # of total followers– Measure of Information exposure
# of tweeted
# of viewed
# of followers
Short-Lived, Ephemeral Fame# of followers# of tweeted
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Case Study : Evolution of #Views
• # of user clicks on the Twitpic URL– limitation : some Twitter clients show the photos
without clicks (no count up)
• Tracing # of view counts – for every hour– 2010.08.15 ~ 2010.08.26– for talkative users
Views of MLB (News)
days
Views of O_CONNECTION (Humor)
days
views
Views of ladygaga (Celebrity)
days
Spreading Tree Analysis
• Using a connected tree from source user• Remove loops, multiple edges
Spreading tree reconstruction
• “RT @Somebody : blah blah”
• General messages
• Reply
Information Spreading Pattern
The median value of properties for trees
Cascade size 17
Max. depth 3
Median depth 1.5
Width 10
Single-edge frac-tion
0.125
Source contribu-tion
0.4375Diffusion trees in Twitter are wide and shal-low.
The source plays an important role in infor-mation diffusion
Source vs the Others
Same # of Tweets, Different Pattenrs of Diffusion
Response Probability
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Social capacity of human beings
• Dunbar’s number
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Dunbar’s number
Behavioral and brain scineces, 16(4):681–735, 1993
The maximum number of social relations managed by modern human is 150.
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#(friends) stimulate interaction?
The more friends one has (up to 200), the more active one is.Median
#(sent msgs)
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Twitter activity vs # of followings
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Caveats
• Not complete from an ego-centric perspective
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Break-up
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Two Sides of Relationship
• Formation and Dissolution– Formation tradiationally well studied– Dissolution hardly much
• Why?– Hard to obtain data
• Proxy for dissolution– No exchange of email [Kossinet09]
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Two Questions We Raise
• How prevalent is unfollow?
• Why do people unfollow?
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Four Types of Tweet
• TweetPSSM is now starting!
• Reply@Virgilio Fantastic Workshop! Thanks for having me!
• MentionI am attending PSSM organized by @Virgilio and @PK!
• RetweetAt UFMG till tomorrow! RT @Virgilio PSSM is now start-ing!
Proportion of Tweet Types
Users become more informational than interactive as the number of followees increases
How Prevalent Is Unfollow?
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Follows and Unfollows
Unfollow is prevalent!
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Unfollow frequent
• Mostly singular– 66% of unfollows are the only unfollow of the day
• But often clustered– 10% with 5 or more other unfollows
• On average– 90% of time intervals between days of unfollow is
less than 9 days
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Communication partner
• Reciprocal and interactive users– Exchange of a mention, a reply, or a retweet and
vice versa
#Comm Partners vs. #Followees
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Passive Nature of Follow
• 85.6% relationships involve no activity• 96.3% involve 3 or fwer• Who unfollows?– Remove 85.6% of no activity and among those
with any activity unfollowed relationships involves less activity than unbroken relationships
Unfollow ratio vs. ego-centric ordering of re-lationship establishments
# Followees vs. # Unfollowees
More Retweets/Favorites Less Likely to Be Unfollowed
The overlap of relationships vs. unfollow ra-tio
Why Do People Unfollow?
47
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Interviews
Q1: Why a participant decided to unfollow.Q2: Whether s/he thought the unfollowee was aware of being unfollowed.Q3: If s/he broke off on other OSNs. Difference?Q4: If s/he followed corporate accounts.Q5: Choose 10 users s/he would never unfollow
Demographics of 22 interviewees
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Q1: Motivations behind Unfollow
• Burst (39)– Burst-only (13), Unintersting topic (10), Mundane
details (6), Automatically generated (4), Conversa-tion (2), Politics (2), Different Views (1), Complains (1)
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Q2: Awareness of Being Unfollowed
• A half of respondents stated that they thought unfollowees were aware of being unfollowed.– They did not know unfollowees in person– They got used to unfollowing– Unfollow was easy
• The other half– Unfollowees had too many followers to notice– No convenient interface to track it– They did not track themselves
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Q3: Break-up on other OSNs?
• Not common
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Q4: Corporate Accounts
• 8 out of 22 follow corporate accounts– 5 kept following– Motivaiton = expectation of prize winning– They didn’t mind occasional ad tweets, but unfol-
low if ads come in bursts– Some only participate if all participants received a
gift
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Q5: Whom Not to Unfollow?
• Most respondents chose intimate friends• Some chose their role models
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Conclusions
• Just a tip of an iceberg for computational – social science– journalism– political science– archeology– literature study– linguistics