Weibo, and a Tale of Two Worlds

Wentao Han1, Xiaowei Zhu1, Ziyan Zhu1, Wenguang Chen1, Weimin Zheng1,Jianguo Lu 2

1Tsinghua University, China

2University of Windsor, Canada

Tshinghua University and University of Windsor

What is Weibo?

Tshinghua University and University of Windsor

User activities of Twitter (42 million) and Weibo (37 million).

User activities of Twitter (42 million) and Weibo (37 million).

Asia

N. Korea

S. Korea

Maldives

Australia

Europe

North America

Weibo and Twitter

Weibo and Twitter platforms

Weibo TwitterMessage length 140 140

Language Chinese (mostly) ManyDirected network yes yes

2000 out-link limit yes yes (with exceptions)

Weibo has hundreds of millions of (registered/active) users.

Mostly disjoint from Twitter users geographically

Not well studied

Only a few studies based on small samplesCompare: two complete Twitter networks were studied (the 2009 network and2014 network)

Tshinghua University and University of Windsor

Goals

Whether are the two worlds similar?

Do the people in the two worlds interact in a similar way?

Whether the world represented by Weibo can reflect the real world?

Tshinghua University and University of Windsor

Experiment

We crawled an almost complete user network

exhaustively follow the out-links;collect users who has at least one in-linkbetween November 2012 - February 2013

282 million nodes

27 billion links

Why is it a complete network?

Few new users could be crawled (689 duplicates to obtain one new user)

Consistent with the official data on the number of registered user

Official registered user size is 500 million;We carried out a uniform random sampling, and find that 40% of the usershave no in-links;282 million are all the users who have in-links

Tshinghua University and University of Windsor

Twitter 2009 data

Publicly available

Both are networks evolved after three years of inception

H. Kwak, C. Lee, H. Park, and S. Moon. What is twitter, a social network ora news media? In WWW, pages 591 - 600. ACM, 2010.

#Nodes #Links Mean Max Std CV Simpson Gini Path SCC(×106) (×109) degree (×106) (×10−4) length (%)

Weibo 222 27 121 25 9028 74 0.25 0.88 3.44 92Twitter 41 1.4 35 2.9 2419 69 1.13 0.83 4.12 80

Table: Statistics of Weibo and Twitter 2009 data. Max, standard deviation (Std),coefficient of variation (CV), Simpson index and Gini index are for in-degrees.

Tshinghua University and University of Windsor

Degrees

In-degree distribution

In-degrees resemble a power law

Exponent is around -1

Degree vs. Rank plot

This degree/rank plot is good for viewing the top bloggers

Not clear for bloggers with small degrees

Tshinghua University and University of Windsor

Degrees

In-degree distribution (freq. vs. degree plot)

For frequency/degree plot, the exponent is around -2 (plus -1 of the previousplot)

Good to view the bloggers with small degrees

Spammers buy in-links in bulks of 10K and 50K.

Tshinghua University and University of Windsor

Degrees

Out-degrees

There are spikes around 2000out-degree limit, for both Weiboand Twitter

Weibo impose more rigid 2000 limit

Tshinghua University and University of Windsor

Degrees

Degrees can tell more information ...

Your friends are richer than you are

Your friends have more friends than you doWeibo: 5567 times moreTwitter: 4761 times moreThis is the coefficient of degree variation

Diversity index: the probability of two links pointing to the same user

Weibo: 0.25 × 10−4

Twitter: 10−4

Gini coefficient, inequality of the in-degrees

Weibo: 0.88Twitter 0.83Higher than the inequality of wealth in any economy

Tshinghua University and University of Windsor

Degrees

Celebrities socialize with celebrities?

Intuitively, yes.

Pearson correlation coefficient between the in-degrees of the followingrelationship is negative.

A tempting conclusion is that popular users tend to follow unpopular usersSurprising result claimed by Myers et al.

S. A. Myers, A. Sharma, P. Gupta, and J. Lin. Information network or socialnetwork?: the structure of the twitter follow graph. In WWW, pages 493 - 498,2014.

Tshinghua University and University of Windsor

Degrees

Celebrities do tend to follow celebrities

When the relation is not linear, Pearson correlation coefficient is not enough

’rich’ follows ’rich’, but ’poor’ also follows ’rich’

Tshinghua University and University of Windsor

Degrees

Possible explanation

Users start by following celebrities (high target degree)

then normal users (target degree drops)

Celebrities do not follow many normal users

Why both Weibo and Twitter bottom at 2000?

Tshinghua University and University of Windsor

Reciprocity

Reciprocity

Reciprocity for most users who have less than 100 in-/out-degrees.Weibo is much lower than Twitter

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Reciprocity

Reciprocity

Overall view of reciprocity for all kinds of people.

Twitter has a much higher reciprocity;

Their reciprocity fluctuates in a similar pattern;

Privileged Twitter users (out − degree > 2000) reciprocate almost everyfollower

Tshinghua University and University of Windsor

Clustering

clustering coefficient

CC is slightly higher in Twitter

Tshinghua University and University of Windsor

Clustering

clustering coefficient

Hypothesis

CC is higher for more economically developed regions

Tshinghua University and University of Windsor

Clustering

top bloggers

107.1

107.2

107.3

107.4

10−3

WeiboAssistant

Weibo Customer ServiceYao Chen

He Jiong

Xie Na

Dee Hsu

Wang Leehom

Zhao Wei

Yang Mi

Kevin Tsai

Zhou Libo

Wen ZhangChen Kun

Barbie Hsu

Kai−Fu Lee

Ruby Lin

Christine Fan

Li Bingbing

Weibo Android Client

Guo Degang

PageR

ank

degree

PageRankProportional to degree

Tshinghua University and University of Windsor

Clustering

pagerank vs. in-degree

107

10−3

PageR

ank

degree

(A)

106

10−3

PageR

ank

degree

(B)

106

107

10−6

10−4

PageR

ank

degree

(C)

105

106

10−5

10−4

10−3

PageR

ank

degree

(D)

Tshinghua University and University of Windsor

Clustering

weibo adoption rate

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

10000

30000

50000

70000

Accounts per capita

GD

P(C

NY

) p

er

ca

pita

AH

BJ

CQ

FJ GD

GS

GX

GZ

HA

HB

HE

HIHLHN

JL

JS

JX

LN

NM

NX

QH

SC

SD

SH

SN

SX

TJ

XJ

XZ

YN

ZJ

Tshinghua University and University of Windsor

Clustering

Connections between regions

−0.05

0

0.05

0.1

0.15

GD AB HK MO FJ TW BJ TJ HE SX SD NM LN JL HL HA XZ QH SN GS NX XJ JX HB HN HI GX OT CQ SC GZ YN SH JS ZJ AH

GD

AB

HK

MO

FJ

TW

BJ

TJ

HE

SX

SD

NM

LN

JL

HL

HA

XZ

QH

SN

GS

NX

XJ

JX

HB

HN

HI

GX

OT

CQ

SC

GZ

YN

SH

JS

ZJ

AH

closet pairs: ChongQing vs SiChuan, Tibet vs QingHai, HongKong vs Macau vsGuangDong

Tshinghua University and University of Windsor

Clustering

Connections between regions

GDAB HKMO FJ TW BJ TJ HE SX SDNMLN JL HL HA XZ QHSNGSNX XJ JX HBHN HI GX OTCQSC GZ YN SH JS ZJ AH0.4

0.42

0.44

0.46

0.48

0.5

0.52

result of running hierarchical agglomerative clustering

Tshinghua University and University of Windsor

Clustering

mutual information of HK and TW

0 5 10 15 20 25 30 35 40−0.15

−0.1

−0.05

0

0.05

0.1

0.15

0.2

0.25

TW

HK

normalized point-wise MI: zero means the strength is the same as in a randomgraph

Tshinghua University and University of Windsor

Clustering

Take away messages

We have the largest OSN user network available for research;

Similarities and differences between the two worlds:

degree variation is similarreciprocity

Connection to the real world

quantify the connection strength between regions

Tshinghua University and University of Windsor