psychology of social media:implication for design
DESCRIPTION
An overview of a social psychological approach to the design of social technologies, with design principles and a brief review of how I applied these principles to several R&D projects in the past few years. This presentation was given to the Seattle chapter of IxDA in October 2009.TRANSCRIPT
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Psychology of Social Media: Implication for Design
Shelly D. Farnham, Ph.D.
Oct 19 2009
IxDA
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My Background: Industry R&D Specialize in social media
Social networks, community, mobile
Early stage innovation Extremely rapid R&D cycle study, brainstorm, design, prototype,
evaluate (repeat)
Career PhD in Social Psych from UW 7 years Microsoft Research 4 years startup world
Personal Map
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Research and Development Process
meeting social goals
Party Report 41%
Invitation 18%
Question 16%
Bond Building 15%
Request 1%
Party report w ith address
9%
Importance of Information in selecting chat partner
0
1
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3
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Rank
Rating
Similarity
Interacts with friends
Ratings by friends
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Waggle Labs: Social Media R&D Consulting and Incubation
CoCollage (Strands)
Swaggle (group text messaging)
Zillow community
Teen Focus Group (MSR)
Social Web 2.0
Pathable
Trusera
Reality AllStarz
Facebook analysis
City of SeattleTwitterdinks
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Core Problem
Human social behavior evolved in different context than what we have today
We are still figuring out how to interact via tech How is it different? How do we make it better?
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Why Interact through Technology? At a distance, over time Access to greater number of people More frequent, continues access Interactions archived Integrate with digital content Identity and context manipulation Large scale collaboration, coordination
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Social Psychological Approach Understanding users
Individuals Social dynamics: pairs, groups,
networks Phenomenological nature of social
experiences Social engineering
Technologies as social environments Technologies as interventions
Focus on supporting social goals Socially intelligent
Use understanding of social processes to inform design
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Example
Design goal: a profile and matchmaking system to increase likelihood of two people finding each other and having a successful dating experience
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Understanding Attraction
Predictors of attraction similarity frequency of exposure Balance theory
Predictors of matching matching hypothesis
Process Reciprocal self-disclosure
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Impact on Design Match on similarity in
demographics, lifestyle Provide opportunities for
frequent exposure, interaction Match based on equivalence in
desirability Put in social context (see
friends, friends of friends) Varying levels of
communication: pseudonymous, identified, asynchronous, realtime
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Design Principles Defining user’s goals
Social goals To like myself That others like me Sense of belonging
Mastery, self-efficacy Implicit vs. explicit
What are People Using Top Facebook Applications For?
community
dating
misc fortunes
naming
social good
play game
play with digital pet
events
media sharing
tell me about me
send gift
profile enhancement
social selection
play social game
social comparison
enhanced communication
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Design Principles
Take perspective of user What is there, and what think is
there, not always the same People respond to what they *think*
is there Behavior is function of person
and situation To predict and change behavior,
must understand all the forces Some internal, some physical,
MANY SOCIAL
http://synapticstimuli.com/wp-content/uploads/2009/05/force_fields.jpg
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Design Principles
The best social technologies are invisible to the user
Need usability, to achieve sociability
Social translucence Visibility, awareness,
accountability
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Influential Early Research
HutchWorld Study:
#1 reason patients used Internet was to interact with family and friends, not to meet other cancer patients/caregivers
Mall Study:
How do people naturally model their social relations? Relationships and groups In terms of importance to self Dynamic and idiosyncratic
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Social Networking, Community, Mobile
User studies
MSR Connections
Personal Map
Point to Point
Wallop
Visualizing and interacting with personal and corporate social networks
Similarity based on interaction behavior, co-occurrence in communication groups
Users found graph visualizations too complicated
How extract meaning of collections/groups?
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Personal MapAutomatically organize contacts in a way that is meaningful/intuitive to user
Shelly Farnham::Will Portnoy
Similarity (A B) = (sum (AB * significance))/sqrt(A * B)Grouped using hierarchical cluster analysis
Infers implicit social groups from communication behavior in email
Provide sense of who’s important
Dynamic, changes as levels of interaction change
Minimal maintenance required
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0
500
1000
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2000
2500
3000
3500
4000
4500
5000
1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49
Size of Distribution List
At Microsoft:75,000 mailing lists,each person belongs to on average 11 mailing lists
Social network info presented relative to self
Shelly Farnham::Will Portnoy
Point to Point User Studiesfacilitate knowledge exchange by exploiting corporate social network information
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Point to Point User Study I
Rank of Similarity to User (1 = Most Similar)
383430262218141062
Prop
orti
on o
n L
ist
.8
.7
.6
.5
.4
.3
.2
.1
0.0
People most similar to the user tended to also be on the user’s list of coworkers.
Rank of Similarity to User (1 = Most Similar)
383430262218141062
Prop
orti
on C
ross
ed O
ff M
ap
.8
.7
.6
.5
.4
.3
.2
.1
0.0
People most similar to the user were not crossed off map
as not belonging.
39 employees completed task Participants listed 15 closest co-workers, used to assess
accuracy of point to point map
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Point to Point User Study II 17 employees completed 16 choices using Point to Point Study design: Participants decided between two randomly selected people
whom they would like to meet for knowledge exchange
Relative Status
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Overlapping People
0
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Unchosen
Chosen
Organizational Distance
10.8
11.1
11.4
11.7
12.0
12.3
12.6
12.9
network information affected decision-making
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Leveraging Social Media for Professional Social Networking Whom do I most want to meet, in the limited
time available to me? Similar to me Complementary skills/needs Notable
How do I meet them?
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Who is here? Who do I want to meet?
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So
cial Netw
orks
So
cial S
cien
tist
Reality
AllS
tar
Med
ia Startu
p
Research startu
p
So
cia
l Tec
h
Blo
gH
er
Blo
gg
er
com
mu
nity
Co
mm
un
ity
blogger
social technology
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Social Networks Online
LinkedIn, MySpace Lists of who’s
connected to whom
Visualizations Graphs
Need more summarization!
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Social Tagging
Add tags as you bookmark Individual
motivation Across people,
importance emerges collective
knowledge Browse people
and related content Tags as pivots
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Exploration at Seattle Mind Camp 3
75 people provided tags for self, organization, related people, related events
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Pathable
Community and social networking tools for conferences
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Design Themes
The event host is a connector and community moderator
Social tags are used as pivots of awareness, connection, and communication
Professional match matching for improved people finding
Incorporate communication back channels
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Face to Face Integration Using existing technologies:
Mobile Badges Printable calendar Visualization
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Personalized Badge
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Match-making Best matches possible, with minimal effort in
profiles Based on predictors of successful matches:
Common interests Same roles
Job title Host provided categories
Co-location By geography By events
Existing shared groups and communities Weighted sum to produce ordered list
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Why Host Cares about Community
We expect that sense of community at events increases attendee loyalty.
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Pathable BarCamp Seattle Study Questions:
how important is social networking at events can Pathable help?
BarCamp Seattle is a free, two-day conference held for Web 2.0
280 people registered for the event using Pathable
78 people total (76% male and 24% female) completed the questionnaire, 18 at the event and 60 afterwards online
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Primary Goal in Coming to Event
0%
10%
20%
30%
40%
50%
60%
70%
80%
Have Fun Be Inspired Learn Meet Others
Primary Goals in Coming to Event
Per
cen
t
People Came to BarCamp Primarily to Network, and then to Learn
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Correlations between Event Features and Intention to Return Will Come Back
Event Feature Next Year
Years to Come
r
r
Number of people met .26 .12
Professional friends at event .31 .01
Satisfaction with sessions .63 .59
Satisfaction with conversations .80 .62
Professional suport .41 .39
Sense of community .44 .78
Event attachment
dependency .62 .73
commitment .67 .79
identificaiton .31 .49
Bolded items are statistically significant at p < .05.
Sense of community and event attachment highly correlation r = .81
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Pathable Usage Everyone registered through Pathable, about half actively used the system
60% actively browsed directory 47% actively browsed messages 19% actively sent messages 43% intended to use directory after event 55% intended to use communication features after event
If they said they came to event only to learn, less likely to use Pathable (t = 2.6, p < .02)
The higher the usage, the more they said it helped them meet people (r = .65, p < .001)
No correlation between usage and count of people met Usage correlated with count of professional friends at event (r = .36, p < .01)
**percentages for those who indicated at least somewhat or quite a bit
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Impact on Professional Network
0
1
2
3
4
5
6
7
8
low high
Impact of Pathable on Size of Network
Num
ber o
f pr
ofes
sion
alfr
iend
s at
eve
nt
Pathable Usage
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Impact on Attachment and Sense of Community
2
3
4
5
6
Event Attachment (Ident) Sense of Community
Low
High
PathableUsage:
Impact of Pathable Usage
Rat
ing
on L
iker
tSc
ale
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Impact of Usage by Feature
Pathable helped attendees meet others the more they browsed the attendee directory
(r = .37, p < .005) the more they browsed attendee messages
(r = .43, p < .005) the more they sent messages
(r =.54, p < .005) the more they used the match-making feature (r = .66, p < .005)
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Themes and Conclusions Mission
Help people meet goals through social technologies
Incorporate psychology of social media Clearly define user goals Examine psycho-social context of technology to influence design Prototyping and *early* deployment to assess technology’s ability to
meet goals
Broad conclusions Important to map natural social processes into social technologies People are *always* seeking to develop social relationships, even in
professional environments Networking and community technologies can and SHOULD meaningfully
impact face-to-face interactions