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A Pattern Language 
for 
New Media 
Gideon Rosenblatt
Six 
Functional 
Blocks 
Organized in many 
different configurations.
Publishing Topic Graph Viewing 
Identity 
Social Graph Revenue
Publishing Content 
Key Variables: 
• Sophistication 
• Openness 
Tools and services for 
creating and publishing 
text, a...
Publishing Sophistication 
Twitter 
Facebook 
(editing, formatting, etc.) 
Google+ 
LinkedIn 
WordPress 
Simpler 
More Com...
Publishing Openness 
(who has access to the ‘publish’ button?) 
Everyone Select Few 
Social Media 
Blogs Magazines
Viewing Content 
Key Variables: 
• Filtering 
Tools and services for filtering, 
viewing, sharing and providing 
feedback ...
Filtering Content 
(who or what filters the content we view?) 
Machine Filtering Professional Filtering 
Algorithms Using ...
Machine Filtering 
(how do we filter the content we view?) 
Topical Interest Social Interest 
Topic Graph Social Graph
Attaching Topics to Content in Publishing 
Publishing 
Topic Graph 
We attach topics to content with 
categories, tags, ke...
Attaching Topics to Content After Publishing 
Topic Graph 
Search Bot 
Knowledge Graph 
Google crawls our content to asses...
Identity: More than Just a Pretty Face 
Identity 
Like many things on the web, our identity is not unlike an 
iceberg; onl...
Attaching Topics to Identity: the Interest Graph 
Identity Topic Graph 
Interest Graph 
Some of that data relates to our i...
Viewing Filtered by Interests 
Interest Graph Viewing 
Our Interest Graph personalizes our search 
results and social medi...
Relationships and Identity: the Social Graph 
Identity Social Graph 
Some of our identity data relates to our relationship...
Viewing Filtered by Relationships 
Social Graph Viewing 
Our Social Graph personalizes our social media 
streams to help u...
Connecting the Topic and Social Graphs 
+ = 
Social Graph Interest Graph Shared Interest 
Graph 
Our Shared Interest Graph...
Revenue from New Media 
Interest Graph > Social Graph 
with Revenues, 
Ads and subscriptions drive most media revenues. Ad...
End User Engagement is the Super Power 
Publishing Social 
Graph 
Interest 
Graph 
Revenues Shared 
Interest 
Graph 
Topic...
Google Plus 
Shared interest network with civil discussion and no revenues. 300 
million users. 
Google ID 
main stream, 
...
Google Search 
Powerful media aggregator, topic graph and ad network. 
1.17 billion users 
Google ID 
search results 
page...
Facebook 
Powerful social network with rich user information. 1.3 billion users. 
Facebook ID 
rich demographics 
stream, ...
LinkedIn 
Powerful professional network and interest graph. 300 million users. 
LinkedIn ID 
rich professional 
history 
s...
Reddit 
Scrappy, content-sharing network, broken out by interests. 114 
million users. 
weak identity 
strong crowd filter...
Huffington Post 
News and opinion, powered by staff-coordinated, unpaid writers. 
84 million users. 
3rd-party logins 
mix...
WordPress 
Blogging / Content Management tool. 77 million websites (22% 
of worldwide total). 
WordPress 
login 
WordPress...
End User Engagement as Super Power 
The real difference with new media is that it is a two-way channel, 
capable of engagi...
For more: 
A Pattern Language 
for New Media 
http://www.the-vital-edge.com/new-media/
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New media pattern language

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New media pattern language

  1. 1. A Pattern Language for New Media Gideon Rosenblatt
  2. 2. Six Functional Blocks Organized in many different configurations.
  3. 3. Publishing Topic Graph Viewing Identity Social Graph Revenue
  4. 4. Publishing Content Key Variables: • Sophistication • Openness Tools and services for creating and publishing text, audio, and video content.
  5. 5. Publishing Sophistication Twitter Facebook (editing, formatting, etc.) Google+ LinkedIn WordPress Simpler More Complex
  6. 6. Publishing Openness (who has access to the ‘publish’ button?) Everyone Select Few Social Media Blogs Magazines
  7. 7. 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.
  8. 8. Filtering Content (who or what filters the content we view?) Machine Filtering Professional Filtering Algorithms Using Social and Topic Graphs Magazine Editors Community & List Moderators
  9. 9. Machine Filtering (how do we filter the content we view?) Topical Interest Social Interest Topic Graph Social Graph
  10. 10. Attaching Topics to Content in Publishing Publishing Topic Graph We attach topics to content with categories, tags, keywords, groups, communities and hashtags.
  11. 11. Attaching Topics to Content After Publishing Topic Graph Search Bot Knowledge Graph 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.
  12. 12. 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.
  13. 13. Attaching Topics to Identity: the Interest Graph Identity Topic Graph Interest 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.
  14. 14. 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.
  15. 15. 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.
  16. 16. Viewing Filtered by Relationships Social Graph Viewing Our Social Graph personalizes our social media streams to help us tend to our relationships.
  17. 17. Connecting the Topic and Social Graphs + = Social Graph Interest Graph Shared Interest Graph Our Shared Interest Graph helps us find and build relationships with people who share our interests.
  18. 18. Revenue from New Media Interest Graph > Social Graph with Revenues, 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.
  19. 19. End User Engagement is the Super Power Publishing Social Graph Interest Graph Revenues Shared Interest Graph Topic Graph Viewing 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.
  20. 20. Google Plus Shared interest network with civil discussion and no revenues. 300 million users. Google ID main stream, circle streams, communities, Google search good search, communities, circles, hashtags posts and comments no revenues, may help search revenues …shared interest graph strangers become friends through a …
  21. 21. Google Search Powerful media aggregator, topic graph and ad network. 1.17 billion users Google ID search results pages search bots, algorithms, Knowledge Graph SEO optimization, Ad Sense search advertising Search, plus Your World relevance algorithms, personalized search
  22. 22. Facebook Powerful social network with rich user information. 1.3 billion users. Facebook ID rich demographics stream, groups Open Graph objects posts & comments, Open Graph for 3rd party sites on-site ads, mobile app Audience Network friends interests groups
  23. 23. LinkedIn Powerful professional network and interest graph. 300 million users. LinkedIn ID rich professional history stream, groups companies, schools occupations, industries updates, rich posts & comments ads to professional audience, premium services professional connections interests school & organizational alumni
  24. 24. Reddit Scrappy, content-sharing network, broken out by interests. 114 million users. weak identity strong crowd filtering, minimal algorithmic filtering categories, ok search Mostly linking, lots of comments minimal ads, freemium friends, but not prominent
  25. 25. Huffington Post News and opinion, powered by staff-coordinated, unpaid writers. 84 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
  26. 26. WordPress Blogging / Content Management tool. 77 million websites (22% of worldwide total). WordPress login WordPress Reader categories, tags, keywords rich editing, commenting freemium, ad-on services relies on 3rd-party social graphs
  27. 27. 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.
  28. 28. For more: A Pattern Language for New Media http://www.the-vital-edge.com/new-media/

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