This document summarizes a presentation about research conducted on the online community Everything2.com. The presentation covered 3 studies: 1. A study of how user tenure relates to participation patterns over time. It found that short-term users were more likely to delete early write-ups and submitting a second write-up increased chances of long-term participation. 2. A study predicting future content contributions using metrics like new write-ups, deleted write-ups, messages, and abandoned accounts. It found it could predict contributions with over 90% accuracy. 3. A proposed study of habitual use of online communities, examining how behaviors become automatic over time based on theories of habit and socio-cognition.

1. “Earn Your Bull$&!*”:
User Lifecycles in
Social Media
Cliff Lampe
Michigan State University
April 22, 2011

2. Cliff Lampe
College of Communication
Arts and Sciences
Dept. of Telecommunication,
Information Studies, and
Media
ACM researcher with a
crunchy Communication
coating
“Socio-technical” Researcher
2

3. How can socio-technical
systems be used to facilitate
collective action?
3

4. Defining terms
Socio-technical
systems
Social Media
Social
Computing
4

5. Collective Action
Collective
Action
Support
Social capital
Shared creation “offline”
generation
outcomes
5

6. Study existing
STS
How can STS be
used to facilitate
Change
collective action? existing STS
Make new
STS
6

7. Other STS Research

8. Study existing STS
Facebook Research
The Online Interaction Lab

9. 9

10. 10

11. 11

12. Creating New STS
Social Media Research Lab

13. 13

14. 14

15. 15

16. 16

17. Everything2.com
Founded in 1998 as everything.blockstackers.com
Spin off of the Slashdot team
Victim of the first dot-com crash
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18. 18

19. 19

20. 20

21. 21

22. 22

23. 23

24. 24

25. Key E2 features
Write-ups = article contribution
Noder = registered site member
Vote = +/- user rating of content
Cool = tag applied by high level users
Catbox = Synchronous chat
Message = asynchronous individual and
group msgs
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26. Earn your Bull%&!*
Earn your bullshit. Node for the other users and not for yourself. Don't let
it be an "attention" node. Once you do a little noding for the higher cause
feel free to take a few nodes to make fun of the French or take a cheap shot
at the Dave Matthews Band or whatever wack idea you wish to spread
throughout the database.
In other words, if you've been a user here for two weeks and have seventy-
three very short writeups you'd best protect yo neck...especially if 90% of them
deal with feces, masturbation or the hallucinatory revelation that came to you
while eating your morning toast...again. If you do that user search on yourself
you may be surprised as to how many have disappeared. We don't need that
anymore!
"Be Cool"
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27. Motivations to
Participate in Online
Communities

28. 28

29. Motivation and participation
Theoretical perspectives
Uses and Gratifications
Organizational Commitment
User types
“Guest” vs. registered
Participation
Future vs. present vs. actual
29

30. Data
Survey
295 anonymous, 304 registered users
Server Logs
Matched for registered users
30

31. Current Future Future
use Use Contribution
Get Info 0.05 0.19 *** -0.03
Provide Info -0.20 ** 0.16 * 0.53 ***
Social
-0.13 . -0.09 0.10
Enhancement
Maintaining
-0.04 -0.13 0.04
Connectivity
Self Discovery 0.08 0.09 0.02
Entertainment 0.22 *** 0.38 *** 0.02
Analysis: OLS Regression, with many controls. R2=0.49, 0.48,
0.56
31

32. Implications
Usability and efficacy didn’t affect the models
Motivations varied widely within both anonymous and
registered users
Motivations were tied to differences in perceived and
actual behavior
32

33. Latent Users and
Motivational
Consistency

34. Two competing views
of user lifecycles
“Born not made”
Panciera et al. 2009, Panciera et al.
2010
“Reader to Leader”
Preece and Shneiderman 2009
34

35. “Born Not Made”
Users come to a site with role predilection
Little change between roles
People fill niches within a type of role
35

36. “Reader to Leader”
Users are socialized into more “weighty”
roles
Users can stop in roles
Roles vs. types
36

37. Method - Interviews
30 active users
Snowball sample to near-saturation
Server log analysis to avoid
homophily
Semi-structured phone interviews
Theme assignment using Atlas.ti
Data matrix
37

38. Inherent Users
Users had made major changes to their
participation practices
Site changes, life changes, user conflict
Previously active participants
Skills, efficacy, context intact
Not lurkers!
38

39. Motivational Consistency
Users reported consistent motivations to participate over
time
Status builders
Personal relationship builders
Community builders
Human capital builders
39

40. Status Builders
‘[...] they were constantly saying
things like ‘you’re doing a great job’,
‘you’re a fantastic writer’ […], that’s
a tremendous amount of motivation
for that sort of material.’ (Rob)
40

41. Personal Relationship Builders
‘I think that I started to see the sort of the social
element was much more prevalent in the content
itself, there were many insider jokes and many more
references of the other people or nodes that
referenced other people […], very soon I got a real
sense of the personalities and I found those people
very attractive. I wanted to have relationships with
them […] that kept me going.’ (Jack).
41

42. Community Builders
‘I guess you’d call it a writing community
[…] everybody reading material, offering
comments, and in turn helping
everybody else becomes a better writer.
[…] It was a great place to basically talk
with like minded people all centered
around writing.’ (Henry).
42

43. Human Capital Builders
‘To hone my technical writing skills
and […] to learn more about science
things.’ (Kim)
‘Like a training wheel for
writing.’ (Rob)
43

44. Exogenous events lead
to behavior changes
Life events (Job, marriage, kid)
Site changes (Raising the Bar, copyright change)
Other site changes (Wikipedia and LiveJournal)
Other user conflicts
44

45. Latent user behaviors
Status builders
Personal relationship builders
Community builders
Human capital builders
45

46. Status builders
Remained regular readers
“I was looking to see if I was missed.” - Oscar
46

47. Relationship Builders
Reading, direct messaging, other channels
‘Most of my friends I got from [Everything2] are
friends in real life, but there are certainly still
people out there that I only see through the site
[…] and it’s probably the only way I would have to
connect with. […] I’d written this thing and thought
that maybe I should uh try it again, post it you
know let some old friends know what’s going
on.’ (Alice).
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48. Community Builders
Move to admin roles, live chat
‘The style of writing that I was good at, was no
longer the style we were focusing on. [I] started
moving towards editing. I would help people
improve their writing instead of trying to put up
Write-ups of my own.’ (Patrick).
48

49. Human capital builders
Feedback to other users
‘So you read some interesting article
and you felt wow! So you give the
authors some feedback email.’ (Bob)
49

50. Born vs. Made
Roles do change, but due to
exogenous factors
There is socialization within roles
Motivation matters
50

51. Implications
Identify, don’t socialize
Use vs. motivation
Match incentives to motivations
Match tools to motivations
Types vs. Roles
51

52. Everything2 studies
in development

53. Study 1:
Effects of User
Tenure
Chandan Sarkar

54. Research Question
How does the time spent on the site relate
to the pattern of participation over time?
54

55. Method
40,324 users divided into 3 categories based on
difference between account creation and last login (time
lagged)
Categories
Short users - 1-87 days
Latent Committed - 88-502 days
Committed - 503-3484 days
55

56. Write-ups trends in first six months from account creation date
56

57. Multinomial regression
The probability of first write-ups deletion is much higher
for the short-term tenure group, compared to the mid-
term tenure group
(Odds ratio= 2.478, p = .03 < .05).
The submission of a second write-up within the
community has a significant effect on the members’ long-
term tenure, compared to mid-term tenure.
(Odds ratio= 3.36, p = .001 < .05).
57

58. Early Interpretations
May be a propensity for tenure as soon as people hit the
site
How much do early experiences shape long term
participation? When are habits formed?
58

59. Study 2:
Predicting Changes
in Productivity
Tor Bjornrud

60. Research Question
Can we predict how much content will be
posted to the site in the near term future?
60

61. Exploratory Analysis
Distributed lag time series predicting change in next week’s number of writeups.
coef p(v) mean std. deviation
(Intercept) 186.17888 ***
New Writeups 0.67468 *** 2094 8180
Deleted Writeups/100 2.60495 * 1115 3234
Message Recipients/100 -9.71485 * 579 238
Message Senders/100 -18.16299 * 247 115
Accounts Abandoned/100 0.04421 0.4986 208 100
Total Messages/100 -0.20698 0.6101 3921 2196
New Cools/100 30.75921 *** 647 356
2
Adjusted R : 0.9507 ***
Signif. codes: *** < 0.001, ** < 0.01, * < 0.05
61

62. New Writeups Per Week
0 500 1000 1500 2000 2500
100
200
300
Time in Weeks
400
500
62

63. Next steps
Figure out some more about why
these features are related.
63

64. Study 3:
Habitual Use of
Online
Communities
Alcides Velasquez and Elif Ozkaya

65. Habit
Dewey (1921) - Habits, emotion and cognition have roles
in behavior
Bandura (1975) - Socio-cognitive theory
LaRose and Eastin (2000) - Internet Self Efficacy
Ozkaya and LaRose (2011) - Internet Habitual Use
65

66. Using everything2.com is something…
I do automatically.
I do without having to consciously remember.
That makes me feel weird if I do not do it.
I do without thinking.
That would require effort not to do it.
I start doing before I realize I’m doing it.
I would find hard not to do.
I have no need to think about doing.
That expresses my personal style
66

67. Other E2 studies...
Detecting roles based on message network
characteristics
Experiments on converting readers to
contributors
Analysis of the effects of meeting offline on
Noders
Responses to the content rating system as a
social behavior feedback mechanism
67

68. “Living
Laboratories”

69. Definition of
Living Labs
Access to server logs
Access to users
Access to feature changes
Active user base
69

70. Chi definitions
Building a system, and studying it in the
laboratory
Adopting a system, and studying it in the
laboratory
Building a system, and studying it in the
wild
Adopting a system and studying it in the
wild
70

71. Examples
MovieLens
Cyclopath
Beehive/Social Blue
Dog Ear
Wiki Dashboard
??
71

72. Pros and Cons
External validity Expensive
Triangulated Risky
data
Generalizability
Persistent trove
Ethical
Known context considerations
of the
community
72

73. E2 Research Team
Rick Wash Tor Bjornrud Chris Hamrick Alcides Velasquez
Akshaya Sreenivasan Elif Ozkaya Chandan Sarkar Yvette Wohn 73

74. Thanks!!
Cliff Lampe
lampecli@msu.edu
http://clifflampe.org
Twitter: @clifflampe
Slideshare:
clifflampe
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