New media pattern language
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Transcript of New media pattern language
A Pattern Language for
New Media
Gideon Rosenblatt
Six Functional
Blocks
Organized in many different
configurations.
Identity
ViewingTopic GraphPublishing
RevenueSocial Graph
Publishing Content
Key Variables:• Sophistication• Openness
Tools and services for creating and publishing text, audio, and video content.
Publishing Sophistication
Google+
WordPress
Simpler
More Complex
(editing, formatting, etc.)
Social Media
Publishing Openness(who has access to the ‘publish’ button?)
Select FewEveryone
Blogs Magazines
Viewing Content
Key Variables:• Filtering
Tools and services for filtering, viewing, sharing and providing feedback on content. Includes social media feeds, search results pages, and aggregating websites.
Algorithms Using Social and Topic
Graphs
Filtering Content(who or what filters the content we view?)
Professional FilteringMachine Filtering
Magazine Editors
Community & List Moderators
Machine Filtering(how do we filter the content we view?)
Social InterestTopical Interest
Topic Graph Social Graph
Attaching Topics to Content in Publishing
Publishing
We attach topics to content with categories, tags, keywords, groups, communities and hashtags.
Topic Graph
Attaching Topics to Content After Publishing
Google crawls our content to assess its relevance to various topics. It also matches what it finds against its Knowledge Graph for more accurate relevance assessment.
Topic Graph
Knowledge Graph
Search Bot
Identity: More than Just a Pretty Face
Identity
Like many things on the web, our identity is not unlike an iceberg; only a small portion of it is visible to us as our profile. The rest resides in massive data sets stored on the proprietary servers of new media companies.
Attaching Topics to Identity: the Interest Graph
Identity Topic Graph
Some of that data relates to our interests. We attach topics to our identity every time we like, plus, tweet, share or search for something online. The result is our own, individual interest graph.
Interest Graph
Viewing Filtered by Interests
ViewingInterest Graph
Our Interest Graph personalizes our search results and social media streams to help us stay on top of our interests.
Relationships and Identity: the Social Graph
Identity Social Graph
Some of our identity data relates to our relationships. We build our social graph by harvesting our past through school and work connections, crawling lists of friends of friends, and making new connections through communities and other online interactions.
Viewing Filtered by Relationships
ViewingSocial Graph
Our Social Graph personalizes our social media streams to help us tend to our relationships.
Connecting the Topic and Social Graphs
+ =
Social Graph Interest GraphShared Interest Graph
Our Shared Interest Graph helps us find and build relationships with people who share our interests.
Revenue from New Media
> Social GraphInterest Graph
Ads and subscriptions drive most media revenues. Ads work best when tied to our interests, but are largely noise when we’re socializing.
Subscription revenues are unlikely from the social graph, unless paired with interests, as in certain shared interest graphs.
with Revenues,
End User Engagement is the Super Power
The following is a simple attempt to apply this pattern language to a handful of representative new media organizations. Of course, others will have a very different interpretation of these organizations.
Publishing SocialGraph
InterestGraph
Revenues SharedInterestGraph
TopicGraph
Viewing
Google ID
main stream, circle
streams,communities,
Google search
good search, communities,
circles, hashtags
posts and comment
s
no revenues,may help
search revenues
…shared interest graph
Google PlusShared interest network with civil discussion and no revenues. 300 million users.
strangers become friends through a
…
Google ID
search results pages
search bots, algorithms,
Knowledge Graph
SEO optimization, Ad
Sense
search advertisingSearch,
plus Your World
Google SearchPowerful media aggregator, topic graph and ad network. 1.17 billion users
relevance algorithms,
personalized search
Facebook IDrich demographics
stream, groups
Open Graph objects
posts & comments, Open
Graph for 3rd party sites
on-site ads, mobile app Audience Network
friends
FacebookPowerful social network with rich user information. 1.3 billion users.
interests
groups
LinkedIn IDrich professional
history
stream, groups
companies, schools occupations,
industries
updates, rich posts &
comments
ads to professional audience, premium servicesprofession
al connection
s
LinkedInPowerful professional network and interest graph. 300 million users.
interests
school & organizational
alumni
weak identity
strong crowd filtering, minimal
algorithmic filtering
categories, ok search
Mostly linking, lots
of comments
minimal ads, freemium
friends, but not
prominent
RedditScrappy, content-sharing network, broken out by interests. 114 million users.
3rd-party logins
mix of editorial and popularity
filtering
60 vertical sites
unpaid bloggers
ads $100 million
relies on 3rd-party social
graphs
Huffington PostNews and opinion, powered by staff-coordinated, unpaid writers. 84 million users.
WordPress login
WordPress Reader
categories, tags,
keywords
rich editing, commentin
g
freemium, ad-on services
relies on 3rd-party social
graphs
WordPressBlogging / Content Management tool. 77 million websites (22% of worldwide total).
End User Engagement as Super Power
The real difference with new media is that it is a two-way channel, capable of engaging people in a new collaborative partnership.
New media success depends on building business models to engage end users in publishing and viewing media, and building social, topical, interest and shared interest graphs.
This is the goose that lays the golden eggs of new media.
For more:
A Pattern Language
for New Media
http://www.the-vital-edge.com/new-media/