Social Web 2.0Implications of Social Technologies for Digital Media

Shelly Farnham, Ph.D.

Com 597 Winter 2007

Week 5 Community Reputation Systems Web 2.0 Revenue Models

Community "I define "community" as networks of interpersonal ties that provide sociability, support,

information, a sense of belonging, and social identity.” Barry Wellman (2001).

“A group of people who share a common interest or purpose; who have the ability to get to know each other better over time. There are two pieces to that definition. That second piece — getting to know each other better over time — means that there needs to be some mechanism of identity and communication.”

Amy Jo Kim (2001)

“1) It is interactive and built on the concept of many-to-many communications ...; 2) It is designed to attract and retain community members who become more than

superficially involved in community events ... and ... are able to make new friends through the community;

3) It has a single defining focus; ... (that) gives them a reason to return;4) It provides services to community members, ... that meet community member needs; 5) It has, or has the potential to develop, a strong commercial element...“

From "Towntalk," a listserv on online community

Socio-Cultural Context

Social dissolution/individualism, lack of traditional communityBob Putnam, “Bowling Alone”

Neo-tribalism Use of Internet to access people,

coordinate

Online Communities

84% of Internet users in U.S. participated in an online community

79% regularly with one particular group 26% to get in touch with local groups

2001 Pew

Providing Value in Terms of User Goals

InformationalLearn about homes to facilitate buying, selling, and improving homes.

Value expression Express my identity around homes. I like my house. Where I live expresses

something about me. I like my agent, and I like my neighborhood.

Social Capitol -- Developing relationships I can leverage laterLearn about, get referrals to, and meet people related to homes (consumers, agents,

neighbors, other vendors).Make friends/friendly acquaintances, find similar others, be liked, have a respected

reputation, be part of a group.

Collective actionFind people with similar interests and organize into groups that can take action around

the group’s agenda. (e.g., neighborhood watch.)

OtherEntertainment: have funSelf-efficacy/mastery

Providing Value through Community

Providing value through access to people.

Common purpose,Identity,

Interactivity

User TrafficSocial Capitol

Why Community Online?

Weak ties, specialized knowledge or circumstances Need sense of shared understanding/frustration Similar others hard to find face to face

Continuous support Sometimes face to face people not in similar situation get bored

with your preoccupation, or just not available all the time Geographical isolation Decreased mobility

increased use for types of problems that impact mobility e.g. knee surgery

Social stigmatization

The Virtual Third Place

The Importance of Place

Places – specific locations in space that provide an anchor and a meaning to who we are.

Orem & Chen, 2003

Proximity primary determinant of liking, through repeated exposure and opportunities for interaction.

Social Psychology

The great good place, neighborhood hangouts and haunts key to development of community. Enabling serendipitous interactions.

Ray Oldenburg, 1989

“Eyes on the street.” Urban planner advocating dense, mixed-use neighborhoods, fostering vibrant urban community and increased security.

Jacobs, 1961

A Sense of Place

Personal identity Community Past and future Being at home “Place is a special and unique location…notable for the fact that the

regular activities of human beings occur there. Moreover, because it is a site of such activities, and all which they entail, it may furnish the basis for our sense of identity, as human beings, as well as for our sense of connection to other human beings, in other words, our sense of community. Place, in other words, is that special site, or sites, in space where people live and work, and where, therefore, they are likely to form intimate and enduring connections.” Orum & Chen p. 15.

Netville Study -- Enabling Neighboring through Technology If neighborhood given the opportunity to

interact/exchange information on the Internet, are they more like to develop neighborhood ties?

Provided high speed internet to 64 out of 109 neighboring homes, two years 1996-1998, with neighborhood email list

Hampton & Wellman, City and Communication: 2:4 December 2003

Netville Study Results

More likely to: know each others’ name, talk on a regular basis, visit each other

Reported familiarity online facilitated meeting face to face Block parties, community gatherings Collective action against the developer

Barry Wellman, Communications of the ACM, 2005, 45, 5. p. 94

What did they talk about?

Discuss interests of common concern (home construction)

Requests for help or advise (e.g. recommendation for a local doctor)

Advertise garage sales, local crafts/services Invitations to community events Messages offering such things as job info

Increased “Eyes on the Street”

Exchange greetings See what is happening Keep watchful eye on children’s activities

2001 MSN Communities Analysis

What are people using discussion groups for?

Type of Community

% of Total Memberships

% of Total Communities

Avg. # of Members

Avg. # of Messages

Avg. # of Photos

Avg. # of Files

Share interest/activity 22% 29% 10 14 23 1Adult 21% 4% 67 18 79 1Dating 17% 5% 42 29 16 1Similar people 13% 13% 14 13 19 1Information exchange 9% 9% 13 16 10 2Self 7% 19% 5 2 30 1Religion 5% 3% 21 55 12 2Family 4% 13% 4 2 35 0Group 2% 3% 9 10 15 1Support 1% 1% 21 32 5 0Humor 0% 1% 6 9 24 2

Average: 14 13 25 1

2001 MSN Communities AnalysisHow does type of group impact measures of

health?

Type of Community

% Members that Post

Community Duration in Days*

Poster Duration in Days*

Number of Messages per Person

Replies per

Message

Adult 13% 143 9 1.9 0.5Dating 19% 88 8 2.6 0.7Similar people 25% 76 9 3.3 0.6Self 30% 31 7 2.5 0.6Information exchange 31% 96 11 3.3 0.6Shared interest/activity 31% 78 12 3.8 0.7Religion 34% 106 16 6.4 0.8Support 35% 137 16 4.1 0.7Group 35% 79 17 2.5 0.5Humor 39% 40 10 3.5 0.6Family 42% 27 7 2.1 0.4

Average: 24% 77 11 3.2 0.6

Online Support Communities

Information flow, exchange, storytelling Group problem solving, insights Trusted sources Decrease worry, anxiety, depression Health:

Improve patient compliance with treatment Info seeking improve decision-making

go to doctor able to talk intelligently about problems, have language for it etc.

assess quality of their care

From Maloney-Krichmar & Preece, In Kneeboard, informational vs. emotional support: giving info (33.5%), opinions (17.4%), suggestions (7.3%), socio-emotional (25.8%)

Online Community General Concerns Access Ease of use Fragmentation Authentication/accountability Commercialism and privacy Safety and security

Bad behavior in online spaces Misappropriation of personal info

Misinformation

Mailing Lists!

Online Community Design Group vs. network form of association

Sense of boundary, you are a member or not Need for active communication

Message board/mailing list Commenting Possible gradation from broadcast to one on one, public to private

Narrow focus vs. broad Tend to succeed with dense groups of similar others Orient similar people around central location (FAQ/wiki/discussion board for

each health issue) Light moderation/hosting of spaces Enabling transition from newbie to mentor

Passing on “host” role Awareness through activity metrics

Time in space Message activity # of stories/lessons posted

Designing for Sociability

Clearly articulated shared purpose Governence, protocols, rituals People

Roles Moderators Experts Lurkers Approx 1% leaders, 19% participate, 80% lurkers

Size Critical mass: number of people needed to make a community

useful Too few not enough, too many overwhelmed Discussion groups: 25 active participants take up all the air

Fostering cooperation

Social dilemma/tragedy of the commons Individual gain vs. collective good

Increasing cooperationWill meet again Identification of behaviorRecord of past behavior

Discovery/Entry Points

Search google Search in system by topic and by person: important to

find similar others Search/show relevant demo factors (SES indicators through job,

college…) Related interests

Entry through invitation to join Invite friends/family/cohorts to view stories etc.

Link off of other community sites Banner ads

Discovery/Entry Points Importance of First Impressions Need to see there is social interaction (social

translucence) exchange/reciprocity shows interpersonal trust Shadows of social behavior: X members, amount recent activity,

new story posts, best story Site trust building:

Post self-regulating policies Privacy and security Editorial and advertising

Source disclosure Third party seal Branding

Communities as intervention

The minimal “intervention”:Define community boundaries

Tapping into personal identity, social identityEnable conversation

Assessment:Measure community growth, participation Impact on neighborhood

Measuring Healthy Community

Health = Function ((presence, content creation, interactivity) * recency *

longevity)

User presence: Many active people in neighborhood Recency Longevity in system

Content creation: Daily posts/comments/tags Rich customization of profiles

Interactivity: Visiting a lot of other people’s pages Long discussion threads

Instrumentation for Social Metadata Treat each behavior as unit of use and

recordUser UserBehavior Timestamp

BehaviorContext BehaviorDetails Aggregate info for sorting etc. Always retain original data for later

analysis/algorithm development

Trust and Reputation Systems

Trust A psychological state comprising

the intention to accept vulnerability based upon positive expectation of the intentions or behavior of another Process-based (past history of

interaction) Character-based (social

similarity) Institution-based

Entity (person, agent) vs content trust

Transitivity Trust in performance (less so) Trust in belief (more so)

Stages of Trust in Site

Preliminary assessment (heuristic, affective) Look and feel of site Branding, familiar, trusted logos etc.

In-depth evaluation of information (analytic) Quality of information Personalization of advice, given by similar

others Long-term relationship with site

From Sillence et al. 2004

Trust in Web Sites Study

Study of 2684 participants examining100 sites, making credibility evaluations

Fogg et al. 2003

Trusted Sites

Content Trust

Factors that impact content trust

Gil & Arch 2006

Content Trust and Related Entities

Gil & Arch 2006

Reputation Systems Online Online interactions outside usual social constraints

(disembodied) Identified behavior History of behavior over time Social context: face-to-face increases normative behavior

People *will* break trust if not held accountable/ prosocial norms not activated by presence of others

Reputation History of past interactions informs current expectation of

reciprocity or retaliation in future Accountability, trust

Reputation Systems -- Key Components Long-lived entities that inspire expectation of

future interaction Capture and distribution of feedback about

current interactions Use of feedback to guide trust decisions Issues:

Low incentive to provide feedback People reluctant to provide negative feedback Ensuring honest reports

Types of Ratings

Implicit Ranking Time in system, frequency of visits, frequency of posts, etc

Explicit Rating Weighted average, explicit rating of object of interest

Collaborative filtering People with similar rating patterns rate this highly, so you will

probably like Assumes high variability in preferences

Peer-based Filter implicit/explicit ratings by relevance to self in network (e.g.

friend of friend)

Importance of Types of Reputation Information

From Jensen et. al 2002, N = ~330

Decision task:Study of use ofreputation informationto inform choice aboutwhom to interactwith

Importance of Types of Reputation Information

From Jensen et. al 2002

Ebay

Ebay

Kuro5hin

Kuro5hin

Kuro5hin

Slashdot

Slashdot

Netscan

Netscan

Netscan

Netscan

Netscan

Behavior of active users in Netscan (top 10%), from Brush et al. 2005

WholeNote

Wholenote Ratings

Design Implications

“Look and feel” matters, at-a-glance judgments impact continuing analysis

Expose “related entities” around any content, with indicators of credibility

Filter both content and reputation metrics by relevance to self -- emphasizing similarity Often reduced overall average ratings the more information is exposed

(voice, picture, profile information): indication of increased discrimination between good/bad, relevant content

Include both implicit and explicit ratings/rankings Expect explicit ratings to be positively biased, so “absence of positive”

matters Ratings per hit rate for example meaningful Count of ratings overall Binary votes: e.g. “useful” or not

Metrics at both level of content and level of author important Rate comments as well as content

Opportunities for Innovation Assessing a person’s/story’s reputation with “others like me” – localized

reputation Under the hood assessment of “trustability” of raters, use to influence their

influence on aggregate scores, search results Recency in system, deviance, phase of treatment, explicit ratings (ratings of

raters) Use interaction history with content to normalize ratings

% of positive ratings out of # of people read/hit vs. simple average Search results, able to change sort by:

Overall ranking/ratings Ranking/rating in my network Similarity/relevance to me Date updated/posted Author

Web 2.0 Revenue Models

Mergers and Acquisitions

Startups get purchased by larger organizations

With minimal expenditure, create:Unique identityHip attitudeAttract a large user base

Advertising

Provide Value

AdvertisingRevenue

User Traffic

Are we providing valuable content that is driving traffic that is leading to advertising revenue?

Google Analytics

Online Advertising Lingo

Page views CPM

Cost per mille (thousand)Usually 2.9$ per thousand views

Ad impressionsAd images presented, around three per page

view CPC

Cost per click throughAnywhere from 10 cents to 85$

Google analytics

Alexa Ranking Info

Subscription Services

Fixed ratePer user per month

Variable ratePay according to level of usageE.g. preferred membership subscriptions

(LinkedIn, Biznik), special search and communication features

Storage (photo sites, imageevents, ) Fixed plus variable

Transaction Commissions

Trading fees Ebay auctions Paypal

per transaction 2.9% + .30

Service commissions Amazon Mechanical Turk Biznik, % of fee for workshops

Aggregation fees ITunes, $ goes to record industry, shave off % per transaction Zazzle/Café Press: notion of base price: e.g. $8.99 for shirt,

designer marks up over and keeps difference Artocracy: 25% of sale (each ~30$) for site

Successful Businesses Keeping market share, critical mass

Patented techniques Google

Hard to recreate data sources Copyrighted content

ITunes, music & video library Secret formulae

Google

From: http://web2.wsj2.com/making_web_20_commercially_successful.htm