Psychology of Social Media:Implication for Design

Psychology of Social Media: Implication for Design

Shelly D. Farnham, Ph.D.

Oct 19 2009

IxDA

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

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

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Rating

Similarity

Interacts with friends

Ratings by friends

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

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?

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

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

Example

Design goal: a profile and matchmaking system to increase likelihood of two people finding each other and having a successful dating experience

Understanding Attraction

Predictors of attraction similarity frequency of exposure Balance theory

Predictors of matching matching hypothesis

Process Reciprocal self-disclosure

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

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

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

Design Principles

The best social technologies are invisible to the user

Need usability, to achieve sociability

Social translucence Visibility, awareness,

accountability

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

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?

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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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

Point to Point User Study I

Rank of Similarity to User (1 = Most Similar)

383430262218141062

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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

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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

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

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Organizational Distance

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network information affected decision-making

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?

Who is here? Who do I want to meet?

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social technology

Social Networks Online

LinkedIn, MySpace Lists of who’s

connected to whom

Visualizations Graphs

Need more summarization!

Social Tagging

Add tags as you bookmark Individual

motivation Across people,

importance emerges collective

knowledge Browse people

and related content Tags as pivots

Exploration at Seattle Mind Camp 3

75 people provided tags for self, organization, related people, related events

Pathable

Community and social networking tools for conferences

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

Face to Face Integration Using existing technologies:

Mobile Badges Printable calendar Visualization

Personalized Badge

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

Why Host Cares about Community

We expect that sense of community at events increases attendee loyalty.

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

Primary Goal in Coming to Event

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Have Fun Be Inspired Learn Meet Others

Primary Goals in Coming to Event

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People Came to BarCamp Primarily to Network, and then to Learn

Correlations between Event Features and Intention to Return Will Come Back

Event Feature Next Year

Years to Come

r

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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

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

Impact on Professional Network

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Impact of Pathable on Size of Network

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Pathable Usage

Impact on Attachment and Sense of Community

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PathableUsage:

Impact of Pathable Usage

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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)

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