(who has access to the ‘publish’ button?)
Everyone Select Few
Tools and services for filtering,
viewing, sharing and providing
feedback on content. Includes
social media feeds, search
results pages, and aggregating
(who or what filters the content we view?)
Machine Filtering Professional Filtering
Algorithms Using Social
and Topic Graphs
Community & List
(how do we filter the content we view?)
Topical Interest Social Interest
Topic Graph Social Graph
Attaching Topics to Content in Publishing
We attach topics to content with
categories, tags, keywords, groups,
communities and hashtags.
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.
Identity: More than Just a Pretty Face
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
Viewing Filtered by Interests
Interest Graph Viewing
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
Social Graph Viewing
Our Social Graph personalizes our social media
streams to help us tend to our relationships.
Connecting the Topic and Social Graphs
Social Graph Interest Graph Shared Interest
Our Shared Interest Graph helps us find and
build relationships with people who share our
Revenue from New Media
Interest Graph > Social Graph
Ads and subscriptions drive most media revenues. Ads work best
when tied to our interests, but are largely noise when we’re
Subscription revenues are unlikely from the social graph, unless
paired with interests, as in certain shared interest graphs.
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.
Shared interest network with civil discussion and no revenues. 300
good search, communities,
may help search
friends through a …
Powerful media aggregator, topic graph and ad network.
1.17 billion users
search bots, algorithms,
Powerful social network with rich user information. 1.3 billion users.
posts & comments,
Open Graph for 3rd
on-site ads, mobile
Powerful professional network and interest graph. 300 million users.
updates, rich posts
ads to professional
Scrappy, content-sharing network, broken out by interests. 114
strong crowd filtering,
linking, lots of
News and opinion, powered by staff-coordinated, unpaid writers.
84 million users.
mix of editorial and
ads $100 million
relies on 3rd-party
Blogging / Content Management tool. 77 million websites (22%
of worldwide total).
relies on 3rd-party
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.
A Pattern Language
for New Media