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THE FUTURE OF CURATED
CONTENT
Chicago Tech Showcase  Webinar
Thoughtly Founder  Chase Perkins
Webinar Host  Zhu-Song Mei
08-27-14
Every person is a content creator,
and every platform is a method of content
distribution.
  
NOISE PROBLEM
There is so much content online that we
created context-specific social networks to
silo content by type.
Academia and Mendeley for academic scholarship.
LinkedIn to publish content in a professional context.
Facebook for friends and family.
Medium for musings.
NOISE PROBLEM
But as the context-specific platforms grow,
they too have a noise problem.
Even though we’ve isolated the content in
silos, we’re still having problems identifying
content at scale.
“A common complaint from Facebook users is that the news feed is
filled with junk that they don’t want to read from news sites known for
pumping out “listicles” featuring subjects like humorous cat videos or
celebrity gossip.”
WSJ, August 25, 2014, Facebook is Cracking Down on Click-Bait –
“What people don’t realize though is that the luck of going viral was
based on a mountain of hard work, on years of effort. There’s a
frustrating truth to success in the Internet age: in order for your work to
reach an audience, someone with power has to give it a chance, and in
order for someone in power to give it a chance, it has to have an
audience.”
Wired, August 25, 2014, Want to Go Viral? It’ll Take a Lot More Work
Than You Think –
  
“On the Internet, there is no limitation to the
number of outlets or voices in the news chorus.
Therefore, quality can easily coexist with crap.
All can thrive in their respective markets. And,
the more noise, confusion, and crap – the more
there is an increase of, and corresponding
need for, trusted guides, respected experts,
and quality brands.”
Marc Andreesen, The Future of the News
Business: A Monumental Twitter Stream all in
One Place –
PROBLEMS IN CONTENT FILTERING
Most content recommenders are based on
collaborative filtering, meaning they use
either…
Popularity-Based Filtering
Good for identifying gossip
and headlines – stuff that’s
trending.
Behavioral Filtering
Finds what your friends and
people like you tend to like.
OR
BUT THERE IS A FUNDAMENTAL
PROBLEM WITH COLLABORATIVE
FILTERING:
There are very few data points and user
interactions associated with new or niche content.
TIMMY VS. SARAH
Timmy has been called a “thought leader” on a social platform.
Sarah is a busy graduate student.
Timmy and Sarah might write an identical piece.
In theory, they should stand an equal chance of being
recognized and gaining traction.
But Timmy is going to win out almost every time.
  
  
In order to identify quality content at scale, you
need a system that looks at the content itself.
You need a system that reads the content it
recommends.
INTELLIBUTLER
A new breed of content recommender and
scheduling mechanism.
Powered by a contextual machine learning
natural language processor.
  
INTELLIBUTLER
Examines several hundred sites and
sources per day, as well as all content
submitted by the IntelliButler community for
analysis.
Recommends content based on its
substance, allowing you to consistently
identify and share great content and carve
out a reputable niche in your field.
  
The Future of Curated Content - Contextual Machine Learning w/ Thoughtly

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The Future of Curated Content - Contextual Machine Learning w/ Thoughtly

  • 1. THE FUTURE OF CURATED CONTENT Chicago Tech Showcase  Webinar Thoughtly Founder  Chase Perkins Webinar Host  Zhu-Song Mei 08-27-14
  • 2. Every person is a content creator, and every platform is a method of content distribution.   
  • 3. NOISE PROBLEM There is so much content online that we created context-specific social networks to silo content by type. Academia and Mendeley for academic scholarship. LinkedIn to publish content in a professional context. Facebook for friends and family. Medium for musings.
  • 4. NOISE PROBLEM But as the context-specific platforms grow, they too have a noise problem. Even though we’ve isolated the content in silos, we’re still having problems identifying content at scale.
  • 5. “A common complaint from Facebook users is that the news feed is filled with junk that they don’t want to read from news sites known for pumping out “listicles” featuring subjects like humorous cat videos or celebrity gossip.” WSJ, August 25, 2014, Facebook is Cracking Down on Click-Bait – “What people don’t realize though is that the luck of going viral was based on a mountain of hard work, on years of effort. There’s a frustrating truth to success in the Internet age: in order for your work to reach an audience, someone with power has to give it a chance, and in order for someone in power to give it a chance, it has to have an audience.” Wired, August 25, 2014, Want to Go Viral? It’ll Take a Lot More Work Than You Think –   
  • 6. “On the Internet, there is no limitation to the number of outlets or voices in the news chorus. Therefore, quality can easily coexist with crap. All can thrive in their respective markets. And, the more noise, confusion, and crap – the more there is an increase of, and corresponding need for, trusted guides, respected experts, and quality brands.” Marc Andreesen, The Future of the News Business: A Monumental Twitter Stream all in One Place –
  • 7. PROBLEMS IN CONTENT FILTERING Most content recommenders are based on collaborative filtering, meaning they use either… Popularity-Based Filtering Good for identifying gossip and headlines – stuff that’s trending. Behavioral Filtering Finds what your friends and people like you tend to like. OR
  • 8. BUT THERE IS A FUNDAMENTAL PROBLEM WITH COLLABORATIVE FILTERING: There are very few data points and user interactions associated with new or niche content.
  • 9. TIMMY VS. SARAH Timmy has been called a “thought leader” on a social platform. Sarah is a busy graduate student. Timmy and Sarah might write an identical piece. In theory, they should stand an equal chance of being recognized and gaining traction. But Timmy is going to win out almost every time.      
  • 10. In order to identify quality content at scale, you need a system that looks at the content itself. You need a system that reads the content it recommends.
  • 11.
  • 12. INTELLIBUTLER A new breed of content recommender and scheduling mechanism. Powered by a contextual machine learning natural language processor.   
  • 13. INTELLIBUTLER Examines several hundred sites and sources per day, as well as all content submitted by the IntelliButler community for analysis. Recommends content based on its substance, allowing you to consistently identify and share great content and carve out a reputable niche in your field.   