Photo by extranoiseThe QCP algorithm uses a combination of metrics to assess the EPC that you will be paid for the traffic you have driven. In addition to looking at the revenue driven by yesterday’s traffic, it also takes into account historical information, such as the long term value of the users that you bring to eBay and the value from sales that happened within the 7 day cookie latency. Therefore, the EPC is comprised of more than just the sales and ACRUs you brought in on any single day.

EPCs will move up or down over time. The more revenue you bring for a certain number of clicks, the higher your EPC will be over time. Less revenue for a certain number of clicks will result in lower EPC. In some cases the full commissions from a sale may not be reflected in your EPC that day. Instead it will serve to increase your overall EPC.

In addition to this, if you increase the number of clicks you are driving to eBay or started a new campaign, then your EPC is likely to fluctuate more than normal as you build up a history. This is because eBay uses revenue generated very close to the click as a way to calculate the value of the new campaign or increased volume of clicks.

Hashtags can be indexed and displayed in your group tweets by first following @hashtags. The service will follow you back automatically.

Second step is to get familiar with hashtag commands, so that you only broadcast a message out to the people you want to receive it, those being the members of your hashtag group.

* Follow #tag – (example: follow #tcot) – lets you follow all updates tagged with #tcot. 

* Follow username#tag – subscribe to all updates from a certain person that are sent to a group. (Example: Follow jeffselig#tcot will give you every post by Jeff sent to the #tcot group)

* #tag message – Send a message or question to the group. (Example: #tcot Anyone know a great software program to organize tweets?)

* #tag !message – Send a message only to people who are subscribed to updates from #tag. (Example: #twitip !How many tcot users post to twitter daily?) 

* Leave #tag – Unsubscribe from the group. If your friends are subscribed to this group as well, you’ll still get messages from them that include updates for the group. (Example: leave #food will unsubscribe you from the Food group, but you’ll still get messages from your best friend and fabulous cook Jenny whenever she sends a message to #food).

* Remove #tag Unsubscribe from the group and from friend messages that include this tag. So if Jenny posts a recipe to #food and you’ve typed Remove #food in your Twitter status bar, you won’t see that recipe even if Jenny is on your followers list.

Is there a better search methodology than linking structures and authority of those links used by Google?

The Bayesian Optimization Algorithm (BOA) is an algorithm based on the estimation of distributions. It uses techniques from modeling data by Bayesian networks to estimating the joint distribution of promising solutions. To obtain the structure of Bayesian network, different search algorithms can be used. The key point that BOA addresses is whether the constructed Bayesian network could generate new and useful solutions (strings), which could lead the algorithm in the right direction to solve the problem. Undoubtedly, this ability is a crucial factor of the efficiency of BOA.

There has been much recent work on optimization algorithms that build probability models based on good solutions found so far and use the constructed models to guide further search. This class of algorithms is called Estimation of distribution algorithms (EDAs). The general scheme of EDAs works as follows:
(1) Develop a probability distribution model by using initial population
(2) Sampling step: generate a data set by sampling from the probability model
(3) Testing step: test the data as solutions to the problem
(4) Selection step: create an improved data set by selecting the better solutions and removing the worse ones
(5) Learning step: create a new probability model from the old model and the improved data set
(6) If the termination criteria are not met, go to (2)

“Naive Bayes” machine learning algorithm. It is a well-studied probabilistic algorithm often used in automatic text categorization. Compared to other algorithms (kNN, SVM, Decision Trees), it’s pretty fast and reasonably competitive in the quality of its results.

BOA Procedure
The procedure of the BOA follows:
(1) Set t=0, randomly generate an initial population P (0)
(2) Select a set of promising strings S (t) from P (t)
(3) Construct a Bayesian network B using a chosen metric
and constraints
(4) Generate a set of new strings O (t) according to the joint
distribution encoded by B
(5) Create a new population P (t+1) by replacing some
strings from P (t) with O (t), set t=t+1
(6) If the termination criteria are not met, go to (2)

Here is the algo in a bit more detail

Creates a new Algorithm::NaiveBayes object and returns it. The following parameters are accepted:

purge

If set to a true value, the do_purge() method will be invoked during train(). The default is true. Set this to a false value if you’d like to be able to add additional instances after training and then call train() again.

add_instance( attributes => HASH, label => STRING|ARRAY )

Adds a training instance to the categorizer. The attributes parameter contains a hash reference whose keys are string attributes and whose values are the weights of those attributes. For instance, if you’re categorizing text documents, the attributes might be the words of the document, and the weights might be the number of times each word occurs in the document.

The label parameter can contain a single string or an array of strings, with each string representing a label for this instance. The labels can be any arbitrary strings. To indicate that a document has no applicable labels, pass an empty array reference.

train()

Calculates the probabilities that will be necessary for categorization using the predict() method.

predict( attributes => HASH )

Use this method to predict the label of an unknown instance. The attributes should be of the same format as you passed to add_instance(). predict() returns a hash reference whose keys are the names of labels, and whose values are the score for each label. Scores are between 0 and 1, where 0 means the label doesn’t seem to apply to this instance, and 1 means it does.

In practice, scores using Naive Bayes tend to be very close to 0 or 1 because of the way normalization is performed. I might try to alleviate this in future versions of the code.

labels()

Returns a list of all the labels the object knows about (in no particular order), or the number of labels if called in a scalar context.

do_purge()

Purges training instances and their associated information from the NaiveBayes object. This can save memory after training.

purge()

Returns true or false depending on the value of the object’s purge property. An optional boolean argument sets the property.

save_state($path)

This object method saves the object to disk for later use. The $path argument indicates the place on disk where the object should be saved:

  $nb->save_state($path);

restore_state($path)

This class method reads the file specified by $path and returns the object that was previously stored there using save_state():

  $nb = Algorithm::NaiveBayes->restore_state($path);

Bayes’ Theorem is a way of inverting a conditional probability. It states:

                P(y|x) P(x)
      P(x|y) = -------------
                   P(y)

The notation P(x|y) means “the probability of x given y.”

In this case, we want to know the probability of a given category given a certain string of words in a document, so we have:

                    P(words | cat) P(cat)
  P(cat | words) = --------------------
                           P(words)

We have applied Bayes’ Theorem because P(cat | words) is a difficult quantity to compute directly, but P(words | cat) and P(cat) are accessible (see below).

The greater the expression above, the greater the probability that the given document belongs to the given category. So we want to find the maximum value. We write this as

                                 P(words | cat) P(cat)
  Best category =   ArgMax      -----------------------
                   cat in cats          P(words)

Since P(words) doesn’t change over the range of categories, we can get rid of it. That’s good, because we didn’t want to have to compute these values anyway. So our new formula is:

  Best category =   ArgMax      P(words | cat) P(cat)
                   cat in cats

Finally, we note that if w1, w2, ... wn are the words in the document, then this expression is equivalent to:

  Best category =   ArgMax      P(w1|cat)*P(w2|cat)*...*P(wn|cat)*P(cat)
                   cat in cats

That’s the formula I use in my document categorization code. The last step is the only non-rigorous one in the derivation, and this is the “naive” part of the Naive Bayes technique. It assumes that the probability of each word appearing in a document is unaffected by the presence or absence of each other word in the document. We assume this even though we know this isn’t true: for example, the word “iodized” is far more likely to appear in a document that contains the word “salt” than it is to appear in a document that contains the word “subroutine”. Luckily, as it turns out, making this assumption even when it isn’t true may have little effect on our results.

One of the star players in Social Media is the “Community Manager”. Responsible for the management and revitalization of communities on a Companies or Causes site.
1 .- Sites embody a community of real people with faces, voices and names. And gradually, they will produce many experiences, the constant generation of experiences that seek to convey the soul of the brand or cause and engage all users.

2 .- The figure of the Community Manager has been highlighted in recent times, especially with the explosion of social networks and their proliferation. The wikipedia defines this figure as the “person in charge of building, growing and managing communities around a brand or cause.” But above all, the community manager is a nexus between the “brand” or “cause”, and the user. In the case of a brand, the person or community team must try to match the target of that brand and transform it into an environment that attracts users. Attempting to meet the objectives of both parties so both are satisfied.

3 .- What are the functions of a Community Manager?

A community manager needs to observe all that is discussed in the previous paragraph and is “down to earth” and turn those functions into everyday tasks. Tasks of many kinds: moderate, dynamic strategies, applications and / or tools, contact the user, social reputation, market research, etc.. But obviously, these tasks depend on many factors: whether it is a brand community or not, whether large or modest, has an impact off-line, objectives, budget, etc.. The more complex the community, managers have more roles and in the case of a trademark is further complicated. All these functions have had to add one more and very important thing to understand the role, what makes a community. Of course, much depends on the brand, its values, its politics, but you must understand that this is a medium that now is being used increasingly by the brands. They have seen communites and social sites potential and want to get into the game, and obviously have to be there to help them have as clear a role as possible, so they also know how to play well together.

4 .- Professionals: Here is the brand itself that acts under their  name. I would give anonymity to the people behind the trademark, but at the same time, allow more than one person take care of things. Staff: it would be a particular employee (or other) of the company or brand, name and surname. There is comcast cares on twitter and then there is eric at comcast.

My opinion is that both models should not be rigid. Depending on the behavior observed or feedback from the user, the intention is to learn from the community and always with them and act accordingly.

5 .- What Social Media tools can you use for a community manger role?

Google Alerts, Tweetbeep, research new pages, applications and trends of the social web. Each day depends on: a new strategy, a new application or a promotion … It would be impossible to list everything.  Regarding the “social media” that you can use, the question is almost what not to use.

6 .- One of the keys to community or social network is to find the “social object”, ie the concept that makes a person decide to enter and be in this community and not in others. The space where real people share the little things that make them happy every day. Create the space where we expect users to share and assume the values of good netizenship.

Web analytics is the measurement, collection, analysis and reporting of internet data for purposes of understanding and optimizing web usage.

Jim Sterne, founder of the Web Analytics Association, said it well in a recent article: “Between the technical expert and the business managers are a new breed: the analysts. An analyst must have technical skills and knowledge as well as management skills and knowledge. They are a mixture of statistical mathematician, technical tactician, business physician, insight generation magician, and master of communication.”

Web analysts make web data into actionable insights and transform marketing performance based on a set of client set KPI’s.

Google analytycs is very good. You can integrate Analytics with Adsense and Adwords if you want, but after that there are a bunch of really great paid services to check out.

Rank	#1	#2	#3	#4	#5	#6	#7	#8	#9	#10
Excellent Very Good Good Fair Poor	Coremetrics	Omniture	WebTrends	Unica	HitsLink	VisiStat	OneStat	Clicky	GoStats	NextSTAT
Overall Rating
Ratings
Features
Traffic Stats
Referrals
Report Stat Intervals
Events
Visitor Details
File Exporting
Help/Support
Features
Multiple Site Monitoring					*
Customizable Dashboard
Bot/Spider Filtering
Social Media Monitoring
Mobile Browser Monitoring
Mobile Access
HTTS Compatible
Multi-language Support				*
Bounce Rates		*
Email Reports						*
Real-time Data
Campaign Tracking						*
Campaign ROI						*
Conversions						*				*
Free Service						*
File Exporting
PDF
CSV
XML
Excel
Word
HTML
Traffic Stats
Page Views
Unique Visitors
Visit History
Entry Pages
Exit Pages
Exit Links
Events per Page
Events per Visit
Time Spent on Page
Time Spent on Site
Avg. Visits per Day
Most Popular Pages
Visitor Path
Referrals
Internal Referrer
Referring Domain
Referring Search Engine
Search Keyworks and Phrases
Report Stat Intervals
Lifetime
Hourly
Daily
Weekly
Monthly
Yearly
Custom Date Range
Events
Click-through
Downloads
Flash Events					*
Multimedia Streams
Error Pages
Favorites/Bookmark Stats
RSS Feed Subscriptions
Visitor Details
Browser
Operating System
Screen Resolution
Cookies Enabled
JavaScript Enabled
Internet Service Provider
IP Address
Organization/Host Name
Username
Country
State/Region
City
Time Zone
Language
Help/Support
Telephone		*
Email
FAQ/Knowledge Base
Online Chat
User Moderated Forums
Training/Tutorials

Coremetrics offers only the hosted (client-side) web metrics solution, which can simplify the choices your organization must consider. They offer a special version of their system for each of these vertical markets: retail, financial, and travel services.

• Go to theCoremetricswebsite
• Read a product review on Infoworld
• Read a case study and interview on a biz that chose Coremetrics.

One of the biggest names in web analytics, Omniture offers a variety of comprehensive web analytics packages.

• Go to the Omniture website

Read a white paper comparing HBX and Omniture’s SIteCatalyst

• Read a product review on Infoworld
• Read a 2009 review in PC Mag
• Look at all of their industry awards

WebTrends is one of the best-known names in web analytics, offering comprehensive tools that can be deployed as client-side (they call this “on-demand”) or server-side (they call this “software”) options. WebTrends is known for being high-end (ie, expensive) but has a variety of packages available.

• Go to the WebTrends website
• Read a 2009 review on CNET
• Read WebTrends Small Business Reviewed on Atlas-Tuesday IT blog
• Read an infoworld review.
• Read 2009 info about various Web Stats Packages on webmasterworld forum.

Unica NetTracker offers client-side (they call it on-demand) and server-side (they call it software) solutions.

• Go to the Unica website
• Read “12 Cool Things You Can Do With Unica’s Netracker Insight”
• Read a customer review of a biz that chose NetTracker
• Read Aug, 2005 info about various Web Stats Packages including NetTracker on webmasterworld forum.

Many of today’s search engines rely on keywords. That is, the user enters the relevant words in your search ( “Albert Einstein” and “Nobel”, eg.), And the application returns all documents containing those words.  Low accuracy or relevancy of the results (will return many irrelevant documents for search: the presence of a keyword in a document does not necessarily imply that the result is relevant).

Excessive sensitivity to the vocabulary used in searches (and, therefore, unable to get to the first relevant results available: many documents of interest may not include keywords, but synonyms, homonyms or antonyms of them).

A study by David Hawking and several researchers assessed 20 conventional search engines (based on keywords) using 54 searches. The percentage of relevant results after inspecting the top 20 websites returned was 0.5% for the best search engine (Northern Light), and Google was the second most precise form. Thus the popularity of keyword-based search engines has little to do with accuracy, but the patience of users.

A semantic search is a query that takes into account the context, and therefore the significance of that for what is being asked (and not just the words of the query), in order to avoid the ambiguities of both the queries as the text of the documents that users are looking for.  For example, a semantic search with the words “discoverer” and “penicillin” would return documents about Alexander Fleming, but these did not appear for these two terms, because it would identify the concepts that structure the search (penicillin is a product that you want to find out discoverer or, more formally, Medicine (Penicillin) Inventor Person (Alexander Fleming)).  The ultimate goal of semantic search is that users can make searches more precise and expressive, giving rise to significant results with minimal user intervention.

Normally, it is accepted that the searches are based on semantic techniques to extract information through the use of ontologies or metadata.  The use of ontologies allows engines to formally define the domains of interest (scientific theories, for example) with an expressive enough foundation so that users can specify their searches in some detail, either before or during executing the query string.

From a technical standpoint, a semantic search engine is an application that includes the searches and the texts of documents on the web by using algorithms that simulate comprehension or understanding, and from them gives accurate results without the user having to open and inspect the document itself. A form of this type recognizes the right context for search words or sentences.  Google or Yahoo search engines are not semantic, since algorithms are mainly based on statistics generated from words and links, not cognitive algorithms that capture the knowledge implicit in the words and context.  For example, a search like “Who was Uranus?”. Some search engines return results related to the seventh planet from the sun, when it is clear that the purpose of the search is to find information about the primordial god of heaven in Greek mythology.

Semantic search engines are not always right the first time the meaning of a polysemous word [A distinction may be made between "true" homonyms]. Therefore, they must have means of disambiguation to know the exact meaning with the word in the search.  For example, a semantic search engine which uses internal ontologies with computer concepts and means of transport must have tools to determine which referred the user when a query with the word bus, or it can mean “digital system that transfers data between components of a computer or computers”. To do this, you can choose the most likely meaning, ask the user to choose among several options (as the search engine Hakia, which has the options of extracting ontology and natural language processing (NLP)) or use the other words in the search to infer the exact meaning of bus in this context (eg., a query such as “What time does the bus Friday from Boston to Stockbridge?”).

As a semantic search engine is based on algorithms that simulate the understanding of words and thus, establish relationships between them, can search user interest in the documents returned but not including the words or search expressions.  For example, a semantic search engine was introduced in the word “marsupial” show documents where they appear terms like: kangaroo, koala, Satanel New Guinea, mountain monkey, rat kangaroo, possum, opossum, Tasmanian devil. As this example shows, the semantic searches are far superior to those based on key words, one can find documents of interest to never find with keywords. Also, if you seek information on various species of marsupials, would not need to formulate the query in different ways, with the name of each species, to obtain the desired information.

The lack of structure and semantic annotation of web resources (Word, PDF, HTML pages, etc..) Requires that the semantic search engine uses algorithms to analyze cognitive resources, word by word, sentence by sentence, to assign words and sentences to ontological concepts. Hence, semantic search engines not covered by now many web resources as conventional, using statistical algorithms, much faster and completely automated.  This limitation will disappear when improving cognitive algorithms as the “semantic islands” unite to form the semantic web, or at least “semantic continents”.

“How to Achieve High Rankings in Google, Yahoo and Bing for Your Business”

Written by Richard Geasey and Shannon Evans

Small business need to be found. Where are they located [maps], what is their phone number and if you have no experience with their product of services, input by those that have checked them out [reviews].

It only took till page ten till I found an error, Google does not do local search PPC by zip code. You can use custom targeting setting to target areas as small as 3 mile radius, but this is the absolute minimum and it is often inaccurate since a visitor’s I.P. address is not always an accurate way to pinpoint someone’s location.

Alright, enough nit picking, after that the rest of the book was smooth sailing.

Each business listing in Google Maps is in fact a giant ‘cluster’ of information combined from  a few different places: Yellow Pages, for example, as well as other third-party providers. However, the basic information that you submit through the Local Business Center is the information that Google trusts the most. This means that it will appear instead of any basic information that Google gets from anywhere else. To make sure the basic information you submit is accurate, Google asks you to verify it first by contacting you at your business address or phone number.

And not just Google but Yahoo, Bing, Yellow Pages, InfoUSA, Local.com, Insider Pages and Yelp review help to mention a few other places to be found.

You can add other information to your listing too — such as a description of your business, for example, as well as photos, reviews, or information about hours and parking costs — that will also appear above similar information from other providers.

If you are a SMB you need this book. The “how to” is essential for any small business to succeed. Improving your overall web presence in today’s market, positioning your business at the top of the search engine results pages [SERP's].

I liked this simple yet informative “how to” be found with local search and can honestly recommend it!

As a G2 [second generation child of not one but two holocaust victims and survivors] I am deeply disappointed by seeing these types of results in Google Suggest.

This function is similar to “Did you mean” that provides alternatives for your query while you type, the difference is that this tool works in real time. By suggesting more detailed searches directly, Google Suggest can make your searches more convenient and efficient without having to reformulate your query.

But if you look a little beyond the use officially promoted and their obvious utility, you can see a powerful tool to identify which keywords and phrases most popular (at the time) on Google, something that marketers of all search engines always dreamed.

So does this mean that Holocaust deniers outweigh truthers searches? The travesty and tragedy that occurred before and during WWII was systematic and held in the following places, none of which show in the results.

Concentration and Extermination camps:

Auschwitz-Birkenau · Bełżec · Bergen-Belsen · Bogdanovka · Buchenwald · Chełmno · Dachau · Gross-Rosen · Herzogenbusch · Janowska · Jasenovac · Kaiserwald · Majdanek · Maly Trostenets · Mauthausen-Gusen · Neuengamme · Ravensbrück · Sachsenhausen · Sajmište · Salaspils · Sobibór · Stutthof · Theresienstadt Treblinka · Uckermark · Warsaw

I could even have dealt with as search suggest results.

Jews in Nazi Germany (1933–1939)

Pogroms: Kristallnacht · Bucharest · Dorohoi · Iaşi · Kaunas · Jedwabne · Lviv (Lvov)

Ghettos: Budapest · Lublin · Lviv · Łódź · Kraków · Kovno · Minsk · Warsaw · Vilna · (List)

Einsatzgruppen: Babi Yar · Rumbula · Ponary · Odessa · Erntefest · Ninth Fort

Final Solution: Wannsee · Operation Reinhard · Holocaust trains · Extermination camps · Madagascar Plan

I find these results particularly disturbing after Googles recent announcement to exit the Chinese search market, and the human rights abuse issues Chinese dissidents face.  So I decided to make a Google Suggest list of my own.  Google may have as a corporate mandate “do no evil” but sometimes they are just ugly

Although not immediately obvious Google Suggest needs to talk to the Google home page to offer suggestions as you type. And whatever you type is controlled by Google’s privacy policies. However, Google Suggest does not base its suggestions on your personal searches, not in your search history.

Here is where the SEO in me comes out. How is it that Google can return fairly accurate [once again Wikipedia comes up #1 in the results page SERP's], but this information is not tied into Google Suggest for ‘fair and balanced’ results. If Google Suggest trends popular search queries why do SERP’s not mimic the queries as these would be most relevant to a user and not based on a linking strategy algorithms.

Additionally, Google does not reveal the reference time range for the total number of searches that are displayed next to each suggestion. For search engine marketers the KPI is to measure the relative difference between possible alternatives.

Photo by gualtieroIt’s ok to say “no” to Twitter if that’s your thing. There’s a chance that it just doesn’t fit into your strategy or help you achieve your goals. That’s cool. However, if it is your thing, you may be interested in how Google ranks tweets. That is if search marketing is your thing.

Do you see Twitter as important to an effective search marketing campaign? Share your thoughts.

Google and Microsoft almost simultaneously announced deals with Twitter a few months back, that would give the companies access to tweets in real-time to fuel their respective search engines’ real-time results. Microsoft immediately launched their version, but it was separate from the regular Bing search engine. Google waited a while, but eventually started incorporating real-time results right into regular Google SERPs (including not only tweets, but various other sources).

After the Twitter deals were announced, Bing came out and said, “If someone has a lot of followers, his/her Tweet may get ranked higher. If a tweet is exactly the same as other Tweets, it will get ranked lower.”

Amit Singhal Google was not as vocal about how it would rank tweets and other real-time results, but the company has now shed a bit of light on that via an interview with MIT’s Technology Review. David Talbot interviewed Google “Fellow” Amit Singhal, who has led development of real-time search at the company. According to him, Google also ranks tweets by followers to an extent, but it’s not just about how many followers you get. It’s about how reputable those followers are.

Singhal likens the system to the well-known Google system of link popularity. Getting good links from reputable sources helps your content in Google, so having followers with that some kind of authority theoretically helps your tweets rank in Google’s real-time search.

“One user following another in social media is analogous to one page linking to another on the Web. Both are a form of recommendation,” Singhal says. “As high-quality pages link to another page on the Web, the quality of the linked-to page goes up. Likewise, in social media, as established users follow another user, the quality of the followed user goes up as well.”

But that’s only one factor.

Do you commonly use hashtags in your tweets? If your goal is to rank in Google’s real-time search index, you may want to cut down on that practice, because according to Singhal, that is a big red flag for a lower quality tweet. This seems to be part of Google’s spam control strategy.

Another noteworthy excerpt from the interview:

Another problem: how, if someone is searching for “Obama,” to sift through White House press tweets and thousands of others to find the most timely and topical information. Google scans tweets to find the “signal in the noise,” he says. Such a “signal” might include a new onslaught of tweets and other blogs that mention “Cambridge police” or “Harry Reid” near mentions of “Obama.” By looking out for such signals, Google is able to furnish real-time hits that contain the freshest subject matter even for very common search terms.

Well, we certainly know more about Google’s strategy for tweet ranking now, but there are still plenty of questions about it. What is Google’s stance is on Ghost Tweeting? Are Google’s ranking factors a good reason to create and follow more Twitter lists in hopes for gaining more reputable industry followers?

The factors mentioned aren’t the only ones Google employs. It’s not like Google is going to tell us everything. It also helps to keep in mind that real-time search spans far beyond just tweets. Still, Twitter is clearly a big part of it, and even the significance of tweets themselves will evolve in time.

Google says it hopes to factor in geo-location data (with regards to tweets) into the real-time search results at some point. Google and Twitter engineers frequently collaborate on  real-time search, which Google itself says is evolving.

By the way, it stands to reason that Google’s strategy for ranking tweets probably shares similarities for how it ranks content from other sources drawn from for real-time search.

“Dunbar’s number is a theoretical cognitive limit to the number of people with whom one can maintain stable social relationships. These are relationships in which an individual knows who each person is, and how each person relates to every other person. Proponents assert that numbers larger than this generally require more restricted rules, laws, and enforced norms to maintain a stable, cohesive group. No precise value has been proposed for Dunbar’s number, but a commonly cited approximation is 150.

Dunbar’s number was first proposed by British anthropologist Robin Dunbar, who theorized that “this limit is a direct function of relative neocortex size, and that this in turn limits group size … the limit imposed by neocortical processing capacity is simply on the number of individuals with whom a stable inter-personal relationship can be maintained.” On the periphery, the number also includes past colleagues such as high school friends with whom a person would want to reacquaint themselves if they met again.” Wikipedia

Speculation upon speculation has led to measurement of the number of workable and sustainable connections for social networks like Facebook, something totally out of logic simply because Facebook makes it possible to “touch” each others friends and followers far beyond Dunbar’s theoretical limit. Facebook enables virtual relationships and the social relations are real, tangible and without any intermediation.

My personal account on twitter, the number of people and companies I follow,  is seven times that of Dunbars Law.  Yet I am able to maintain these relationships using groups settings, siloing activities, interests and prioritizing communications.

The hierarchies and limits do not exist outside of our corporeal entities, they are fantasies that tell us how close we are to each other in terms of similarity as a measure of narcissisim. This categorization of social relations are established primarily as a measure of the affinity I have with other network members. But remember that there is another dimension, the real, which measures the geographical separation.

Tweetups and meetups, $5k charity road races add another hierarchy completely different from the first of its origin and is a natural gradient of the spatial distribution of the availability of resources to meet our physiological needs in the social space. Simply said “IRL”, In Real Life is evolving location based social networking beyond simple mapping of events and places. Where online transcends geographical connectivity presenting presence based social networking.

Social networks of direct interaction recurrently makes us put our feet on the ground, because the affinity is an ideal. If we consider a limit to the social network of direct interaction, say the number of Dunbar, that limit will establish a border between the material of human relations from a local and a global dimension.

While this transition is discrete, abrupt changes are from 1 to 150, since the other dimensions are incorporated, the network is definitely moving away from reality and becomes purely virtual greatly extending theoretical Dunbar limits.

The Value of Being a Connector

Gladwell speaks of Dunbars Number in the Tipping Point.  The greater numerical value attributed to a person the higher propensity towards creating a groundswell around their communications within their circle of dominant influence, multiplied by their followers sphere of influence.