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

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Personal Correspondents

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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?

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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