Transcript What is Twitter, a Social Network or a News Media?

TWITTER
What is Twitter,
a Social Network or a News
Media?
Haewoon Kwak Changhyun Lee Hosung Park
Moon
Sue
Department of Computer Science, KAIST, Korea
19th International World Wide Web Conference (WWW2010)
TWITTER
Twitter, a microblog service
write a short message
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TWITTER
Twitter, a microblog service
read neighbors’ tweets
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TWITTER
In most OSN
“We are friends.”
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TWITTER
In Twitter
“I follow you.”
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TWITTER
Following on Twitter
“Unlike most social networks, following on
Twitter is not mutual. Someone who thinks
you're interesting can follow you, and you don't
have to approve, or follow back."
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http://help.twitter.com/entries/14019-what-is-following
TWITTER
Following = subscribing
tweets
recent tweets of followings
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TWITTER
http://blog.marsdencartoons.com/2009/06/18/cartoon-iranian-election-demonstrations-and-twitter/marsden-iran-twitter72/
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PROBLEM STATEME
The goal of this work
• We analyze how directed relations of following
set Twitter apart from existing OSNs.
• Then, we see if Twitter has any characteristics
of news media.
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TWITTER
me⋅di⋅a [mee-dee-uh]
1.a pl. of medium
2.the means of communication, as
radio and television, newspapers, and
magazines, that reach or influence
people widely
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http://dictionary.reference.com/
PROBLEM STATEME
The goal of this work
• We analyze how directed relations of following
set Twitter apart from existing OSNs.
• Then, we see if Twitter has any characteristics
of news media.
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1. Following is mostly not reciprocated (not so “social”)
2. Users talk about timely topics
3. A few users reach large audience directly
4. Most users can reach large audience by WOM*
quickly
5. *WOM: word-of-mouth
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OUTLIN
Summary of our findings
TWITTER
Data collection
(2009/06/01~09/24)
• 41.7M user profiles (near-complete at that
time)
• 1.47B following relations
• 4262 trending topics
• 106M tweets mentioning trending topics
‣ Spam tweets removed by CleanTweets
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*http://an.kaist.ac.kr/traces/WWW2010.html
TWITTER
How we crawled
• Twitter’s well-defined 3rd party API
• With 20+ ‘whitelisted’ IPs
‣ Send total 20,000 requests per IP / hour
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TWITTER
Recent studies
• Ranking methodologies [WSDM’10]
• Predicting movie profits [HYPERTEXT’10]
• Recommending users [CHI’10 microblogging]
• Detecting real time events [WWW’10]
• The ‘entire’ Twittersphere unexplored
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TRANSITION
Part I.
1. Following is mostly not reciprocated (not so
“social”)
2. Users talk about timely topics
3. A few users reach large audience directly
4. Most users can reach large audience by WOM*
quickly
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2. ACTIVE SUBSCRIPTIO
Why do people follow others?
• Reflection of offline social relationships
otherwise,
• Subscription to others’ messages
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2. ACTIVE SUBSCRIPTIO
Sociologists’ answer
• “Reciprocal interactions pervade every relation
of primitive life and in all social systems”
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2. ACTIVE SUBSCRIPTIO
Is following reciprocal?
• Only 22.1% of user pairs follow each other
• Much lower than
‣ 68% on Flickr
‣ 84% on Yahoo! 360
‣ 77% on Cyworld guestbook messages
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2. ACTIVE SUBSCRIPTIO
Low reciprocity of following
• Following is not similarly used as friend in
OSNs
‣ Not really reflection of offline social
relationships
• Active subscription of tweets!
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TRANSITION
Part II.
1. Following is mostly not reciprocated (not so
“social”)
2. Users talk about timely topics
3. A few users reach large audience directly
4. Most users can reach large audience by WOM*
quickly
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1. TIMELINESS TOPICS
Dynamically changing
trends
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1. TIMELINESS TOPICS
User participation pattern
can be a signature of a topic
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1. TIMELINESS TOPICS
Majority of topics are
headline
31.5%
“ephemeral”
54.3%
“headline news”
6.9%
7.3%
“persistent news”
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TRANSITION
Part III.
1. Following is mostly not reciprocated (not so
“social”)
2. Users talk about timely topics
3. A few users reach large audience directly
4. Most users can reach large audience by WOM*
quickly
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3. A FEW HUB
How many followers a user has?
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3. A FEW HUB
CCDF
• Complementary Cumulative Density Function
• CCDF(x=k) = P(x)dx
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3. A FEW HUB
Reading the graph
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3. A FEW HUB
Plenty of super-hubs
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3. A FEW HUB
More super-hubs than
projected by power-law
• Where do they get all the followers? Possibly
from...
‣ Search by ‘name’
‣ Recommendation by Twitter
• They reach millions in one hop
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3. A FEW HUB
Are those who have many
followers active?
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3. A FEW HUB
How we plotted
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3. A FEW HUB
How we plotted
=9
× Med.
Avg. = 8
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3. A FEW HUB
More followers, more tweets
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3. A FEW HUB
Many followers without activity
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3. A FEW HUB
Twitter user rankings by
Followers, PageRank and RT
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3. A FEW HUB
Twitter user rankings by
Followers, PageRank and RT
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Kandal’s tau
rank correlation
3. A FEW HUB
Great discrepancy among
rankings
# followers (RF), PageRank (RPR)
# retweets (RRT )
Top k ranking
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TRANSITION
Part IV.
1. Following is mostly not reciprocated (not so “social”)
2. Users talk about timely topics
3. A few users reach large audience directly
4. Most users can reach large audience by WOM*
quickly
5. *WOM: word-of-mouth
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4. WORD-OF-MOUTH
Which is more efficient for
WOM?
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4. WORD-OF-MOUTH
In Twitter
Information
Following
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4. WORD-OF-MOUTH
Average path length: 4.1
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4. WORD-OF-MOUTH
Retweet (RT)
• Relay tweets from a following to followers
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4. WORD-OF-MOUTH
Retweet (RT)
• Relay tweets from a following to followers
Last day of WWW’10
node 0
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4. WORD-OF-MOUTH
Retweet (RT)
• Relay tweets from a following to followers
Last day of WWW’10
Last day of WWW’10
Last day of WWW’10
Last day of WWW’10
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4. WORD-OF-MOUTH
Retweet (RT)
• Relay tweets from a following to followers
RT @node0 Last day of WWW’10
node 4
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4. WORD-OF-MOUTH
Retweet (RT)
• Relay tweets from a following to followers
RT @node0 Last day of WWW’10
RT @node0 Last day of WWW’10
RT @node0 Last day of WWW’10
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4. WORD-OF-MOUTH
Retweet (RT)
• Relay tweets from a following to followers
retweeter
r
w writer
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4. WORD-OF-MOUTH
Retweet (RT)
• Not only 1 hop neighbors
1 hop neighbors
r
w
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4. WORD-OF-MOUTH
Retweet (RT)
• More goes further
2 hop neighbors
r
w
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4. WORD-OF-MOUTH
We construct RT tree
• A tree with writer and retweeter(s)
r
w
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4. WORD-OF-MOUTH
Height of RT trees
W
W
r
r r r
W
r
r
r
1
1
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2
4. WORD-OF-MOUTH
Empirical RT trees
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4. WORD-OF-MOUTH
96% of RT trees = Height 1
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4. WORD-OF-MOUTH
Boosting audience by RT
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4. WORD-OF-MOUTH
Additional readers
2 additional readers
by retweeter
3 followers
r
w
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4. WORD-OF-MOUTH
A retweet brings a few
hundred additional readers
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4. WORD-OF-MOUTH
Time lag between hops in RT
tree
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4. WORD-OF-MOUTH
Fast relaying tweets by RT:
35% of RT < 10 min.
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4. WORD-OF-MOUTH
Fast relaying tweets by RT:
55% of RT < 1hr.
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SUMMARY
Summary
1. We study the entire Twittersphere
2. Low reciprocity distinguishes Twitter from OSNs
3. Twitter has characteristics of news media:
‣ Tweets mentioning timely topics
‣ Plenty of hubs reaching a large public directly
‣ Fast and wide spread of word-of-mouth
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