Transcript Preferential Behavior in Online Groups

Preferential Behavior in
Online Groups
Lars Backstrom, Ravi Kumar, Cameron
Marlow, Jasmine Novak, Andrew Tomkins
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Power Users
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Executive Summary of Preferential Treatment
Long term power-users are:
1) 20 times more likely to receive a response upon joining
2) Twice as likely to receive a response upon becoming
heavily engaged
3) 9 times more likely to have early responses come from
other power-users
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Outline
• Introduction to online groups
• Experimental set-up
• Statistics of group “cores”
• Statistics of heavily engaged users
• Preferential treatment of engaged users
• Model: Predicting deep engagement
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Outline
• Introduction to online groups
• Experimental set-up
• Statistics of group “cores”
• Statistics of heavily engaged users
• Preferential treatment of engaged users
• Model: Predicting deep engagement
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Online Groups
• A majority of internet users participate in some form of
“online group” related to hobbies, beliefs or offline
relationships (Pew 2001)
• Groups vary along a number of dimensions:
–
–
–
–
Scale
Online vs. offline relationships
Broadcast, Q/A, and interaction
etc.
• Examples
– Ithaca Rotary Club mailing list
– Palo Alto Parenting group
– Aerosmith fan club on MySpace
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Yahoo Groups
• 100 million users, 6 million groups
• Can be created by any user. This user becomes the
moderator of the group
– controls privacy settings, access, memberships, etc.
• Content includes information pages, multimedia content,
and message boards.
• Majority of contents resides in message boards.
– Members may post a message on a new topic, or respond to a
message posted earlier
– Users may read content online, or receive by email
– ~6 million groups, ~6 billion messages
• We used data from one year: May 2005 - May 2006
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Privacy and Size
• Analysis performed on several categories of groups
• Size
– Small: fewer than 20 unique posters
– Medium: 20-99 unique posters
– Large: greater than 100 posters
• Privacy
– Public: open and listed or open and unlisted
– Semi-public: restricted and listed
– Private: closed and listed, closed and unlisted or restricted and
unlisted
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Yahoo Groups
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Outline
• Introduction to online groups
• Experimental set-up
• Statistics of group “cores”
• Statistics of heavily engaged users
• Preferential treatment of engaged users
• Model: Predicting deep engagement
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Engaged Users & Thriving Groups
• Various degrees of user engagement.
– lurkers, to heavily engaged
• Our focus: users who are heavily engaged in the group,
with a high level of posting activity
• What differentiates these engaged users? Are they treated
differently? Do they behave differently?
• Look at “thriving” groups
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Thriving Groups
Three requirements to be a “thriving” group:
1) Baseline Users
– At least 10 users must post during the year
2) Baseline traffic
– at least two messages for every 30 day window.
3) Dense period
– a two-month period during which every 7-day interval has at least
10 posts
New corpus: 44,473 groups, 1M users
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k-Cores
• We define the k-core of a group at time t as follows:
• For a two week window around t, a user is in the k-core if:
– the user has replied to k distinct users in the group
– the user has been replied to by k distinct users
3-core user
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Outline
• Introduction to online groups
• Experimental set-up
• Statistics of group “cores”
• Statistics of heavily engaged users
• Preferential treatment of engaged users
• Model: Predicting deep engagement
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Core Size
48% of group/time
pairs have a 2-core of
at least 6 people
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Fraction of Posters in Core
1
0.9
0.8
0.7
0.6
Private
Semi-public
Public
0.5
0.4
0.3
0.2
0.1
0
Small
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Medium
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Large
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Time Spent in Core
large public
groups: 94%
in core less
than 2 weeks
small private
groups: 48% in
core for 200+ days
small-private
groups: 20% in
core less than
2 weeks
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Half-life of Cores
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Core Populations
• Light: briefly enters the
conversation, i.e., don’t
enter the core
• Short Core: enters the
core for less than 50 days
Long
core
90k
Short core
134k
Light 774k
• Long Core: enters the
core for 50 days or more
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Outline
• Introduction to online groups
• Experimental set-up
• Statistics of group “cores”
• Statistics of heavily engaged users
• Preferential treatment of engaged users
• Model: Predicting deep engagement
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Long Core Users Across Groups
(6, 0.55) = long-core in
first 6 groups joined, 55%
probability of being
long-core in the 7th
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Multiple Memberships
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Outline
• Introduction to online groups
• Experimental set-up
• Statistics of group “cores”
• Statistics of heavily engaged users
• Preferential treatment of engaged users
• Model: Predicting deep engagement
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Preferential Treatment of Engaged Users
•
Are engaged, or “long-core”, users treated differently
within a group?
•
Yes! We detail three key forms of preferential treatment
given to heavily engaged users.
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Response to Newcomer
Private Small
Public Medium
Semi-Public Small
Private Large
Public Small
Semi-Public Large
Private Medium
Public Large
Semi-public Medium
0.5
0.45
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
Light
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Short-Core
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Long-core
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Response to Core Members
0.35
0.3
0.25
0.2
Public
Semi-Public
Private
0.15
0.1
0.05
0
Long-core
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Short-core
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Probability
Response Probability by Newcomer Type
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
Long-core
Column 2
Light
Light
Short-core
Long-core
Newcomer Type
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Long-core Response Probability
0.34
0.33
0.32
Public
Semi-Public
Private
0.31
0.3
0.29
0.28
At time of
joining core
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When in Core
50+ Days
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Summary of Preferential Treatment
Heavily engaged “long-core” users are:
1) 20 times more likely to receive a response upon joining
2) Twice as likely to receive a response upon becoming
heavily engaged
3) 9 times more likely to have early responses come from
other long-core users
Note: Probability of receiving a response increases until
joining the core, then begins to decline.
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First Post Types
100 first posts of long-core
users:
Friends 6%
Friends: the newcomer has
some prior relationship with
another group member
Introduction: the new poster
introduces herself to the
group
No decision
57%
Introduction
37%
No decision: no information
to determine a relationship
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Pregnancy-and-pups: Coccidiosis
I am new to this board and I have enjoyed reading the posts. I
am hoping you can help me learn more about coccidiosis.
I have a litter of puppies whose stool is good. They have
been on Albon for 10 days. One of the puppies went home
and to the vet today and has coccidiosis. Why is that? What
else can I use to be sure the puppies are free of cocci?
I do appreciate all your input and all your time in helping me!
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skatefans: Appropos of Barbara Cook
Hi! This is my first "Skatefans" post. (I've been reading -- don't
like the word "lurking" -- Skatefans since about the summer of
2000, but haven't had the time to post before.)
While everyone, of course, is entitled to their own likes and
dislikes, I'd just like to add some thoughts about "Fosse" from
someone who's been a very big fan of that program. I've only
been following figure skating since 1997 so my frame of
reference is obviously limited but, in terms of the exhibition
programs that I've seen duing this period, I think "Fosse" is
one of the "landmark" exhibition programs that I've seen
(although I can also see some of the problems with it that
people have been pointing out).
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Outline
• Introduction to online groups
• Experimental set-up
• Examination of group “cores”
• Statistics of heavily engaged users
• Preferential treatment of engaged users
• Model: Predicting deep engagement
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Modeling Long-Core Engagement
Factors at work creating long-core engagement:
• User factor: a user’s personality causes her to become
long-core in every group she joins
• Group factor: a group is so welcoming, or its topic so
engaging, that users are likely to become long-core
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Model
Users
Groups
Chance of being
long-core in a
group: 0.7
Chance of random
member being a
long-core user: 0.3
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Model
• For each (u,g) pair, predict whether pair is in set of
memberships H that are long-core.
• Pr[(u,g) in H] = 1 - (1-p(u))(1-p(g))
• Task is to choose the best p(u) and p(g) to reproduce H,
the set of long-core memberships
• Evaluate quality by the likelihood of predicting H
• Consider three variants:
– Use only properties of users, p(g) = 0
– Use only properties of groups, p(u) = 0
– Allow both p(u) and p(g) to be arbitrary
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Analytical Results
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Model
% of correct edges
User-only p(g) = 0
94.9
Group-only p(u) = 0
85.6
Combined
95.1
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Improvement Using Group Factor
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Fin
• Social analysis of one of the world’s largest collections of
online communities
• Proposed a partitioning of the data to select for active
communities of engaged users
• Examined several levels of engagement: “light”, “shortcore”, and “long-core”
• Identified several striking ways in which heavily engaged
users are given preferential treatment from other members
of the group
• Proposed a model to study factors contributing to long-term
engagement and showed that both user and group factors
play a role.
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Fin++
Special thanks to the Groups team:
Di-fa Chang, Lee Clancy, David Kopp, Bobby Lee,
Maria Saltz and Gordon Strause.
Ravi Kumar, Jasmine Novak & Andrew Tomkins
{ravikuma,jnovak,atomkins}@yahoo-inc.com
Lars Backstrom [email protected]
Cameron Marlow [email protected]
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