Let’s Learn Agent Rank and Reputational Scores…it’s about Content and Writers and Panda.
So here we are, almost 3 months since the “Big” Panda Poop of Feb 24th…and still, no light at the end of the tunnel for those who got pooped on by the Panda Update.
It’s 1:40am…and I still have a few posts in my head, and I can’t sleep until I get at least one of them out of my head and into my blog.
Remember the 23 questions that Amit said we should ask ourselves for guidance on building high quality sites? (and remember my initial responses?)
Let’s look at these specific 4 questions:
Is this article written by an expert or enthusiast who knows the topic well, or is it more shallow in nature?
How much quality control is done on content?
Is the site a recognized authority on its topic?
Is the content mass-produced by or outsourced to a large number of creators, or spread across a large network of sites, so that individual pages or sites don’t get as much attention or care?
There questions remind me of a Google Patent application called Agent Rank.
I hate quoting patents because the average Joe might not be able to follow…but hey, maybe my readers aren’t average Joes…there’s a lot of goodness, and I had to cut back on the amount that I wanted to quote here….but here’s some of the juiciest parts that relate to what I’m going to get at in a moment…Big Disclosure…. I changed the word “Agent” in the below section to be the word “Writer”… I kept replacing the words in my head, thought I’d make reading easier on you if I just replaced those words….maybe I’m wrong to do this…but you can always replace the word back in your head if you’d like…hehe…
 The name of the writer can be used to influence the ranking of web search results by indicating the writer responsible for a particular content piece. In one implementation, the reputation for a writer is expressed as a numerical score. A high reputational score indicates that the writer has an established positive reputation. The reputational scores of two or more writers can be compared, and the writer having the higher reputational score can be considered to be more authoritative. In an alternative implementation, multiple scores can be computed for different contexts. For example, a writer might have a first score for content that the writer has written, and a second score for content that the writer has reviewed. In another example, a writer that is responsible for an entertainment magazine could have a high reputation score for content related to celebrity news, but a low reputation score for content related to professional medical advice.
 Assuming that a given writer has a high reputational score, representing an established reputation for authoring valuable content, then additional content authored and signed by that writer will be promoted relative to unsigned content or content from less reputable writers in search results. Similarly, if the signer has a large reputational score due to the writer having an established reputation for providing accurate reviews, the rank of the referenced content can be raised accordingly.
 A high reputational score need not give a writer the ability to manipulate web search rankings. In one implementation, reputational scores are relatively difficult to increase and relatively easy to decrease, creating a disincentive for a writer to place its reputation at risk by endorsing content inappropriately. Since the signatures of reputable writers can be used to promote the ranking of signed content in web search results, writers have a powerful incentive to establish and maintain a good reputational score.
 In one implementation, a writer’s reputation can be derived using a relative ranking algorithm, e.g., Google’s PageRank as set forth in U.S. Pat. No. 6,285,999, based on the content bearing the writer’s signature. Using such an algorithm, a writer’s reputation can be determined from the extrinsic relationships between writers as well as content. Intuitively, a writer should have a higher reputational score, regardless of the content signed by the writer, if the content signed by the writer is frequently referenced by other writers or content. Not all references, however, are necessarily of equal significance. For example, a reference by another writer with a high reputational score is of greater significance than a reference by another writer with a low reputational score. Thus, the reputation of a particular writer, and therefore the reputational score assigned to the particular writer, should depend not just on the number of references to the content signed by the particular writer, but on the importance of the referring documents and other writers. This implies a recursive definition: the reputation of a particular writer is a function of the reputation of the content and writers which refer to it.
Bill Slawski has talked about Agent Rank before, and in November of 2010 Bill said:
The Agent Rank approach hinges upon every publisher on the Web having a unique digital signature, that can follow them around from one site to another.
Write a blog post on your blog – you sign it with your digital signature.
Write a guest blog post on someone elses blog – again, you sign it with your digital signature.
Leave a comment on a blog you’ve never seen before – you attach your digital signature to it.
Your “reputation” follows you around to different sources, and the ranking of things you write, whether on your own pages or those owned by others, can be influenced by a reputation score for your work.
A lot of the ideas that I’ve been thinking of the past few days have been derived from Agent Rank. It’s late….and I probably shouldn’t go into details about the ideas that I have based off of Agent Rank….so I guess I’ll have to break Amit’s Rule #7 (Does the article provide original content or information, original reporting, original research, or original analysis?) and leave off any original analysis….I probably should blog about my thoughts and ideas on this at 2:26am….So I lead you to some water….drink up…and I’ll leave the thoughts to you….
What do you think?…and what ideas do you have? (Leave a comment please).
OK…now I can go to sleep and dream dreams about my content army of “expert enthusiast” ninja writers…and Agent Rank 😉
oh…my comments are all on moderation unless you’ve been approved… I’ll approve a few times each day…just a heads up.
PS..Reminder, we’re changing names to Internet Marketing Ninjas in a few months.
- Google Panda Update – User Behavior and Other Signals – Examines user behavior as a factor, query entry, the
SERP behavior, and click backs. Also looks into when you can get out of being Pandasized.
- Thoughts and Solutions from Jim Boykin – Post discussing the background of the Panda Update, including the supplemental
results, caffine, and beyond. This post also looks at, ” if I were Google what I would look at,” and at solutions.
- Google Panda Update – A Overview of Analytics of 5 Panda II Affected Sites – This post discusses the analytics
of five affected sites
- Google Panda Update – Google’s Content Guidence and Jim’s Take – This article goes over the list of 23 questions of
the ‘Google MMindset’ for the Panda Update and outlines Jims thoughts for each.
- Google Panda Update Panda’s Punitive Punishment of Good Content – This post discuses how the Panda will punish your
good content if fyou have bad content as well
- Google Panda Update – Short Clicks and Long Clicks / Pogosticking – this post talks about how Google uses its logs and
click information, particularly for short clicks, long clicks, and pogosticking to help evaluate and rerank search results.
The post further discusses potential implications on the Panda update.
- Losing Clients to Panda. I Just Lost $17,500/Month – Sharing of experience on the Panda Update as well as reflection
on previous updates
- Google Panda Update: Content + Design = Usable, Trustworthy Websites – discussion of website usability and implications
on Google Panda Update
- Google Panda Update: Your Site is Going to Survive (funny) – Jim’s Panda Update remake of, “Country Boy Can Survive”