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Week 4 slides from the class "Social Web 2.0" I taught at the University of Washington's Masters in Communication program in 2007. Most of the content is still very relevant today. Topics: Social networks, privacy.
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Social Web 2.0Implications of Social Technologies for Digital Media
Shelly Farnham, Ph.D.
Com 597 Winter 2007
Week 4 Social Networks Privacy
Social Networks Defined
Set of pair-wise relationships vs. individual relationships or groups
Social network analysis examine density, boundedness, size,
heterogeneity Study flows of influence, information, social
supportNetwork vs. group social structure
Social Networks
Measuring Ties Ties: friendship, role sharing, common event,
common property, co-occurrence Tie strength: affection, frequency of interaction, trust,
frequency of co-occurrence
Network structures singletons, stars, middle region, giant Tend to be reciprocal (84% in yahoo, 70% in flickr)
Social Networks Today
Modern transformation in how things get done communication, collaboration, information flow
Social networks most appropriate model Lateral, not hierarchical command chains Ad hoc teams, not static groups
Developing social capital – the value of social networks
NetWORK It’s not what you know, it’s who you know Intensional networks
Building Maintaining Activating when need to get work done
Social Networks Online
100 million on MySpace, 80% of total
Why Articulate Social Networks Online
Social networks contextualizes information and behavior Increase accountability by putting people back in social
context Informational context, understanding author Exploit transitive trust (implicit and explicit referrals) Increase relevance via similarity/affiliation Provide access to weak ties Define access/sharing/subscription (filter out as much as
increase access)
Navigation tool Browse network vs. directed search Info transfer horizontal, across hierarchical boundaries
Social Networks Online cont’d
Processing outside user awareness: Alternative “similarity” measures aside from explicit friend/family lists:
Cross-links Communication patterns Co-occurrence in groups Co-occurrence of semantic tags
Prioritize match-making by distance in network “We recommend you check out Jon’s story…” Closer is better, # of overlap is better, etc.
Network/cluster analysis, use for prioritizing search results developing semantic hierarchies Extraction of groups (dense, tightly bounded networks)
Isolate connectors Identify people connected across network clusters, able to transfer info/trust
Online Social Networking Issues
Often binary (friend/no friend) with friend list glut Assume one network per person, no subnets causing role conflict Social capital of “connector” lost Systems do not expect social networks to be dynamic, become out
of date “Now what” – ok so I built my network, now what? No cross-property integration, building network over and over Developing critical mass Visualizations often outside comprehension of average user
Teens use
55% of online teens use social networks 66% of those have private profile 48% visit daily More common in older teen girls (70%) than boys (54%)
Why? Friends as center of life, 91% say to keep up with friends Stage of life: expanding network Do not have face-to-face access (parental control) Manage communications outside email (less spam)
Implicit Social Networks
Based on who’s interacting with whom Provide sense of who’s important to whom Dynamic, changes as levels of interaction
change Minimal maintenance required
Personal Map
Point to Point
Inner Circle
Wallop
managing, knowledge seeking, communication and sharing managing, knowledge seeking, communication and sharing
Personal MapAutomatically organize contacts in a way that is meaningful/intuitive to user
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
Shelly Farnham::Will Portnoy
Similarity (A B) = (sum (AB * significance))/sqrt(A * B)Grouped using hierarchical cluster analysis
Personal Map User StudyPersonal Map
Not at All Extremely So
765432
Fre
quency
8
6
4
2
0
Accuracy 15 MS employees
85% spent no time organizing contacts
contacts not very organized (M = 2.3)
They liked the Personal Map (M = 5.1) “wow” “that’s cool” “makes more sense sooner
than the contact list” They did not find it confusing
or difficult to use (M = 2.9) They rated it as very accurate
(M = 5.7)
on scale of 1 = not at all to 7 = extremely so
Point to Pointfacilitate knowledge exchange by exploiting corporate social network information
0
500
1000
1500
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 selfShelly Farnham::Will Portnoy
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
Point to Point User Study II 17 employees completed 16 choices using Point to Point Decide between two randomly selected people whom you would like to
meet for knowledge exchange
Relative Status
0
2
4
6
8
10
12
Overlapping People
0
0.5
1
1.5
2
2.5
3
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
Point to Point User Study II
2 3 4 5 6 7
Job typeSimilarity to me in job title
Job Status (e.g., PM vs. Lead PM)Whether I know their team
Whether I know themWether they know someone I know
Whether they appear to be an expertNumber of reports
Nearness to me in corporation
% People Use Method
How Often Use Method*
Methods used to learn about a person:Company's email address book 100% 4.5Internal/external web searches 59% 3.1Ask co-workers 35% 2.8Company's web-based org chart 24% 3.0
Methods used to learn about a project or group:Internal/external web searches 82% 4.0Ask co-workers 47% 3.7
*where 1 = Never and 5 = Always
How People Find InformationHow people currently find information about people
and groups within their corporation.
Self-reported importance of features in deciding whom participants would meet (where 1 = not at all and 7 = extremely so).
Wallop embed interactions in social context to activate prosocial norms
Sean Kelly :: Shelly Farnham :: Alwin Vynmeister :: Richard Hughes :: Will Portnoy :: Ryszard Kott :: Lili Sean Kelly :: Shelly Farnham :: Alwin Vynmeister :: Richard Hughes :: Will Portnoy :: Ryszard Kott :: Lili ChengCheng
Blog, share media, build conversations in context of social network
Use communication and sharing behavior to build implicit network
Use network to define scope of search, notifications, sharing
Wallop:Large Scale Deployment
August 01 2004 – present
Over 47,000 registered users
26% become active, logging in and adding content at least once a week
72% users in Chinese Time zone
Communication and Sharing Behavior of Active Users
0 2 4 6 8 10
Mp3s
Blog entries
Images
Comments
Logins
Activity Count per Week
8.1
4.7
2.4
1.9
.6
Wallop Basic Usage Statistics People building conversations,
responding to each other’s content Total threads with > 1
messages: 47,074 ~38% of blog entries have a
threaded conversation Average thread length: 2.98 Average # participants: 2.53 Longest thread: 40
People customizing look of blog 48% active users have selected
profile image 23% have selected a background
image
Wallop Basic Usage StatisticsSocial Network of Active Users
Average number of people in visible network: 8.25
People wanted ability to explicitly add/remove people, but did not use too heavily Each user explicity include 1.7
people on their network Each user explicitly excluded .7
people from their network
Size of network largely determined by invite quota: r = .56
Total Network Size
363228242016128.04.0.00
Count
4000
3000
2000
1000
0
Managing Viral Growth Invite only
membership Tiered invite process
Limited invites by generations to optimize “seed” person’s network.
1st Generation: 10 invites2nd Generation: 5 invites3rd Generation: 5 invites4th Generation 0 invites
Managing Viral GrowthGoal: linear system growth Daily/lifetime activity
quotas
Daily recapture of invite quota from inactive users
Prioritize and promote healthy users for granting invite quota requests
Invite allocation = Function (System cap - Current registered users - Outstanding Invitation Liability)
Promoting Healthy UsersHealth = Function ((logins, content creation, commenting) * recency *
longevity)
Characteristics of a Healthy User Many active contacts in network Daily posts with pictures and
music Multiple comments from contacts
on each post Rich customization of profile and
blog Visits lots of other people’s
pages Long discussion threads
Wallop Users Feedback Users value the visual appeal of the user interface The interface is very cool!
The incredible ease of use and just plain "coolness" factor that allow me and friends who do not live close to interact on a daily basis
“Expressing myself” rated most important reason for use of Wallop, over sharing with friends and meeting new people I found we Chinese are really poor at expressing our passion, especially for
our family members, we love them indeed, but we are not able or dare not to speak it out. Fortunetely, wallop has provided me such a chance to record my feeling down. It's a great tool, sometimes i think it's amazing, disantce don't exist here, we can go everywhere in the community.
Needed better tools for managing bad behavior Implemented blocking, and protecting network: public but read only On average, 8% of active people have blocked someone (3 times each), and
8% have protected their network
Chinese users, UI problems and language problems
Wallop Deployment Lessons
Implicit network effective for bootstrapping, low maintenance Communication and invitations most useful measures
of connection People still want ability add/remove/pin people
People valued identity play and social interactions Personalization and value expressive features rated
most important by users Conversation around blogs and media actively used
feature
Design Implications Building social network should not be an end in
itself -- for users task is not grow network but define who I share with define who I watch Share who I know to help others find info/support
Users want to see how people are connected, provides context
Network info should be used to prioritize/structure information
Build in referrals, intros through network Reflect dynamism of relationships, multiplicity of
networks Simplify user interface to relevant data
Friendster Home Page
Dating:HookupsDirect PesteringFamiliar Stranger
FakestersCulturalGroupsPassing
FaceBook Home Page
FaceBook Home Page
Wallop Home Page
MySpace Home Page
Tribe Home Page
Tribe Tribe page
MySpace Invite
Build through invitations
Myspace Add Friend
Add person when viewing profile
Network used Primarily to find similar others(in same crowd,Same age etc)For dating
LinkedIn Invite
Building businessRelationships, Transitive trust important
LinkedIn – Social Network
Users want to see how connected
Friend of a friend meaningful,not beyond that
LinkedIn -- Add
LinkedIn Introduction Chain
Emphasis on Similarity by Org, used to define access etc.
Evite – managing network
Evite -- inviting
Gmail – managing network
Email still the “killer app”
Inner Circle (from MSR)Goal: provide easy
access to communication history and shared documents according to important people and groups
Infers importance from transaction history
Authorship and sharing history natural way to organize information
Sharing Models
PrivacyThe right and desire of a person to control
disclosure of personal health information Sharing models
Models for defining who has access to what information
Opposing tensions Desire to learn and share vs. privacy concerns
Privacy
Personally identifying information Identifies online persona with real world you
First name, last name Pictures SS #, credit card numbers Re-identification
Can piece together is neighbor Joe because he’s the only one with prostrate cancer in small town of springfield
Must be Jane because she’s the only person with breast cancer who’s a patient of Doctor Smith in Cincinatti
Sensitivity of privacy info Potential for abuse by industries, employers Shame, embarrassment
Design Implications -- privacy Identity
Anonymity Pseudonimity Within-system identity
(need transition to out of system) Privacy through aggregation Access controls
Perimeter definition By class, by person, by similar others, everyone, by data type, by
organization Access levels
password protected, unlisted, public/searchable Level of detail by distance in network
Strong tie, weak tie, 1st, 2nd 3rd degree Default settings: site should be more conservative than users
Design implications -- sharing
When sharing, people often less concerned with privacy than they say People tend to go with default settings Favor easy options over wise
Users want sense of control, sense of who sees what Make audience known, concrete Want to see what audience sees
Requires social intelligence Plausible deniability about status, not in your face you are not
categorized as “friend” Do not “delete” people, remove them from sharing list
MySpace privacy settings
FaceBook Privacy settings
Facebook – privacy settings
Facebook -- privacy
LinkedIn Privacy Settings
See friend of friend but not beyond
Profile fields, none basic or full
Social Networks in an Age of Web 2.0 FOAF