2012-11-13: cognoto pitch
Post on 21-Oct-2014
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Identifying online malicious content
cognoto.com
The Big Problem
Have you ever read a New York Times article and then looked at the comment section in the hopes of finding a thought-provoking discussion only to be let down by the hundreds of posts of profanity laced with rants and personal attacks?
The Big Problem
1. The web has become flooded with destructive posts that ruin the user experience
2. Companies have to employ moderators either in house or outsourced to prescreen User Generated Content (UCG) like comments
Scientific American July 25th, 2012These days, online comments "are extraordinarily aggressive, without resolving anything," said Art Markman, a professor of psychology at the University of Texas at Austin….
“Media outlets should cut down on the anger and hatred that have become the norm in reader exchanges.”
"If on a website comments are left up that are making personal attacks in the nastiest way, you're sending the message that this is acceptable human behavior."
http://www.scientificamerican.com/article.cfm?id=why-is-everyone-on-the-internet-so-angry
BBC Sept. 14, 2011
“Trolling is a phenomenon that has swept across websites in recent years. Online forums, Facebook pages and newspaper comment forms are bombarded with insults, provocations or threats.”
“The answer is for newspaper websites and online forums to employ sufficient moderators to prevent the comments spiraling into petty vendettas”
www.bbc.co.uk/news/magazine-14898564
Online Community
BrandParticipants in the
Community
Observers Other Stakeholders in the Community
Toxic Post
Future Participants Advertisers
Partners
ReputationUsers
Content Creators
Media
Authority
The Moderation Market
Some companies, including The New York Times and Huffington Post, still moderate their own websites, but the costs of employing full-time moderators can add up.
Often, a brand will simply abandon control of their comments; Npr.org hired ICUC last year to manage all its commenting.
www.businessweek.com/magazine/comment-moderator-the-dirtiest-job-on-the-internet-12012011.html#p2
The Moderation Market
Moderation outsourcing services will continue to grow, predicts Jeremiah Owyang, an analyst specializing in online customer service with the Altimeter Group.
Community Management firms charge clients up to $50k per month for moderation services
Individual Moderators can earn $40k-$80k per year
www.businessweek.com/magazine/comment-moderator-the-dirtiest-job-on-the-internet-12012011.html#p2
The Solution
To help communities more efficiently manage user generated content through a customizable service that analyzes word and phrase patterns. (Semantic Analysis)
Ability to place UGC into categories and can provide a score of how confident the engine is of that placement
Good Bad
Known "Good" to us.
Unknown to "Cognoto"
1 2
Training Testing
Known "Bad" to us.
Unknown to "Cognoto"
Who Are Our Customers?
Providers of User Generated Content- Media Sites with comment sections including Huffington Post, NYTimes, Youtube- Retail sites with Product Reviews including Amazon, Newegg, eBay, and Zappos- Misc. including Yelp and Google MapsContent Management Service Providers such as Demand Media, LiveFyre, or Disqus
Fact Check: Huffington Post has a team of nearly 30 moderators that look after more than 9 million pieces of user generated content (comments) each month!
Business Model
B2B Service
We provide content providers (like NYTimes, Huffpost) with a tool to help manage user generated content
Provide scalable but customized solution
Experiment
...Thats my business idea, but I am still having some doubts. Do guys think that is a stupid idea?
That is not stupid. That is f***ing awesome.
That is f***ing stupid. That is not awesome.
Looking at key words will not always give you the proper insight.
"good" +1
"bad" -1
"not good" -1
"not bad" +1
"no, that is not good" +1
"no, that is not bad" -1
"yeah, right" +1
+1 x +1 = +1-1 x +1 = -1-1 x -1 = +1
wrong
wrong
wrong
Think classifying by words is easy? Think again.
Simple +/- keyword analysis
A B C D E
Lorem ipsum dolor sit amet, consectetur adipiscing elit.
60% 40% 45% 53% 54%
Good vs Bad
G B
Bad
A - Proprietary MethodB - UnigramC - BigramD - TrigramE - Naive Bayes Classifier...
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