Tag: facebook

Facebook and R

Part 1 How do people at Facebook use R?

tamar Rosenn, Facebook

Itamar conveyed how Facebook’s Data Team used R in 2007 to answer two questions about new users: (i) which data points predict whether a user will stay? and (ii) if they stay, which data points predict how active they’ll be after three months?

For the first question, Itamar’s team used recursive partitioning (via the rpartpackage) to infer that just two data points are significantly predictive of whether a user remains on Facebook: (i) having more than one session as a new user, and (ii) entering basic profile information.

For the second question, they fit the data to a logistic model using a least angle regression approach (via the lars package), and found that activity at three months was predicted by variables related to three classes of behavior: (i) how often a user was reached out to by others, (ii) frequency of third party application use, and (iii) what Itamar termed “receptiveness” — related to how forthcoming a user was on the site.

source-http://www.dataspora.com/2009/02/predictive-analytics-using-r/

and cute graphs like the famous

https://www.facebook.com/notes/facebook-engineering/visualizing-friendships/469716398919

 

and

studying baseball on facebook

https://www.facebook.com/notes/facebook-data-team/baseball-on-facebook/10150142265858859

by counting the number of posts that occurred the day after a team lost divided by the total number of wins, since losses for great teams are remarkable and since winning teams’ fans just post more.

 

But mostly at

https://www.facebook.com/data?sk=notes and https://www.facebook.com/data?v=app_4949752878

 

and creating new packages

1. jjplot (not much action here!)

https://r-forge.r-project.org/scm/viewvc.php/?root=jjplot

though

I liked the promise of JJplot at

http://pleasescoopme.com/2010/03/31/using-jjplot-to-explore-tipping-behavior/

2. ising models

https://github.com/slycoder/Rflim

https://www.facebook.com/note.php?note_id=10150359708746212

3. R pipe

https://github.com/slycoder/Rpipe

 

even the FB interns are cool

http://brenocon.com/blog/2009/02/comparison-of-data-analysis-packages-r-matlab-scipy-excel-sas-spss-stata/

 

Part 2 How do people with R use Facebook?

Using the API at https://developers.facebook.com/tools/explorer

and code mashes from

 

http://romainfrancois.blog.free.fr/index.php?post/2012/01/15/Crawling-facebook-with-R

http://applyr.blogspot.in/2012/01/mining-facebook-data-most-liked-status.html

but the wonderful troubleshooting code from http://www.brocktibert.com/blog/2012/01/19/358/

which needs to be added to the code first

 

and using network package

>access_token=”XXXXXXXXXXXX”

Annoyingly the Facebook token can expire after some time, this can lead to huge wait and NULL results with Oauth errors

If that happens you need to regenerate the token

What we need
> require(RCurl)
> require(rjson)
> download.file(url=”http://curl.haxx.se/ca/cacert.pem”, destfile=”cacert.pem”)

Roman’s Famous Facebook Function (altered)

> facebook <- function( path = “me”, access_token , options){
+ if( !missing(options) ){
+ options <- sprintf( “?%s”, paste( names(options), “=”, unlist(options), collapse = “&”, sep = “” ) )
+ } else {
+ options <- “”
+ }
+ data <- getURL( sprintf( “https://graph.facebook.com/%s%s&access_token=%s&#8221;, path, options, access_token ), cainfo=”cacert.pem” )
+ fromJSON( data )
+ }

 

Now getting the friends list
> friends <- facebook( path=”me/friends” , access_token=access_token)
> # extract Facebook IDs
> friends.id <- sapply(friends$data, function(x) x$id)
> # extract names
> friends.name <- sapply(friends$data, function(x) iconv(x$name,”UTF-8″,”ASCII//TRANSLIT”))
> # short names to initials
> initials <- function(x) paste(substr(x,1,1), collapse=””)
> friends.initial <- sapply(strsplit(friends.name,” “), initials)

This matrix can take a long time to build, so you can change the value of N to say 40 to test your network. I needed to press the escape button to cut short the plotting of all 400 friends of mine.
> # friendship relation matrix
> N <- length(friends.id)
> friendship.matrix <- matrix(0,N,N)
> for (i in 1:N) {
+ tmp <- facebook( path=paste(“me/mutualfriends”, friends.id[i], sep=”/”) , access_token=access_token)
+ mutualfriends <- sapply(tmp$data, function(x) x$id)
+ friendship.matrix[i,friends.id %in% mutualfriends] <- 1
+ }

 

Plotting using Network package in R (with help from the  comments at http://applyr.blogspot.in/2012/01/mining-facebook-data-most-liked-status.html)

> require(network)

>net1<- as.network(friendship.matrix)

> plot(net1, label=friends.initial, arrowhead.cex=0)

(Rgraphviz is tough if you are on Windows 7 like me)

but there is an alternative igraph solution at https://github.com/sciruela/facebookFriends/blob/master/facebook.r

 

After all that-..talk.. a graph..of my Facebook Network with friends initials as labels..

 

Opinion piece-

I hope plans to make the Facebook R package get fulfilled (just as the twitteR  package led to many interesting analysis)

and also Linkedin has an API at http://developer.linkedin.com/apis

I think it would be interesting to plot professional relationships across social networks as well. But I hope to see a LinkedIn package (or blog code) soon.

As for jjplot, I had hoped ggplot and jjplot merged or atleast had some kind of inclusion in the Deducer GUI. Maybe a Google Summer of Code project if people are busy!!

Also the geeks at Facebook.com can think of giving something back to the R community, as Google generously does with funding packages like RUnit, Deducer and Summer of Code, besides sponsoring meet ups etc.

 

(note – this is part of the research for the upcoming book ” R for Business Analytics”)

 

ps-

but didnt get time to download all my posts using R code at

https://gist.github.com/1634662#

or do specific Facebook Page analysis using R at

http://tonybreyal.wordpress.com/2012/01/06/r-web-scraping-r-bloggers-facebook-page-to-gain-further-information-about-an-authors-r-blog-posts-e-g-number-of-likes-comments-shares-etc/

Updated-

 #access token from https://developers.facebook.com/tools/explorer
access_token="AAuFgaOcVaUZAssCvL9dPbZCjghTEwwhNxZAwpLdZCbw6xw7gARYoWnPHxihO1DcJgSSahd67LgZDZD"
require(RCurl)
 require(rjson)
# download the file needed for authentication http://www.brocktibert.com/blog/2012/01/19/358/
download.file(url="http://curl.haxx.se/ca/cacert.pem", destfile="cacert.pem")
# http://romainfrancois.blog.free.fr/index.php?post/2012/01/15/Crawling-facebook-with-R
facebook <- function( path = "me", access_token = token, options){
if( !missing(options) ){
options <- sprintf( "?%s", paste( names(options), "=", unlist(options), collapse = "&", sep = "" ) )
} else {
options <- ""
}
data <- getURL( sprintf( "https://graph.facebook.com/%s%s&access_token=%s", path, options, access_token ), cainfo="cacert.pem" )
fromJSON( data )
}

 # see http://applyr.blogspot.in/2012/01/mining-facebook-data-most-liked-status.html

# scrape the list of friends
friends <- facebook( path="me/friends" , access_token=access_token)
# extract Facebook IDs
friends.id <- sapply(friends$data, function(x) x$id)
# extract names 
friends.name <- sapply(friends$data, function(x)  iconv(x$name,"UTF-8","ASCII//TRANSLIT"))
# short names to initials 
initials <- function(x) paste(substr(x,1,1), collapse="")
friends.initial <- sapply(strsplit(friends.name," "), initials)

# friendship relation matrix
#N <- length(friends.id)
N <- 200
friendship.matrix <- matrix(0,N,N)
for (i in 1:N) {
  tmp <- facebook( path=paste("me/mutualfriends", friends.id[i], sep="/") , access_token=access_token)
  mutualfriends <- sapply(tmp$data, function(x) x$id)
  friendship.matrix[i,friends.id %in% mutualfriends] <- 1
}
require(network)
net1<- as.network(friendship.matrix)
plot(net1, label=friends.initial, arrowhead.cex=0)

Created by Pretty R at inside-R.org

Facebook IPO- Do you feel lucky?

2 Jan 2011 dealbook.nytimes.com

Facebook has raised $500 million from Goldman Sachs and a Russian investor in a transaction that values the company at $50 billion

29 Jan 2011 -www.bloomberg.com-$82.9-billion

14 Jun 2011-CNBC———————-$100 billion

27 Jun 2011 -news.cnet.com———-$70 billion

27 Sep 2011-Venturebeat.com——-$82.5 billion

100 billion valuation divided by 1000 million subscribers

=100 $ net present value of ad profit (note if 80 billion valuation with 800 million subscribers it is the same)

=250 $ net present value of ad revenues (assuming 40 % profitability)

=2500 $ net present value of online purchases by Facebook ad clicking customer

(assuming advertisers dedicate 10% of revenue to advertising by Facebook)

and the lucky Russian Investor who invested at 50 billion valuation only to see it double in six months, where else has he inVested

http://nymag.com/daily/intel/2011/01/facebooks_russian_investor_hel.html

Digital Sky Technologies co-founder Yuri Milner, who co-invested in the Goldman-Facebook deal, enviably poised in the middle. DST has been investing early and aggressively in some of the biggest names in the tech bubble boom like Facebook (DST first invested in May 2009), Zynga (the company that makes Farmville and Cityville for Facebook), and Groupon (the dudes that just turned down Google’s $6 billion).

NOTE -Both groupon and Zynga IPO  investors lost money as they are now below IPO price.

http://openchannel.msnbc.msn.com/_news/2011/01/05/5771129-russian-facebook-investors-have-sparked-us-concerns

More on Digital Sky Tech and Yuri Milner and the free internet in Putin’s Russia

Digital Sky got particular attention because of its broad control of the Russian Internet. DNI noted that the company is “a dominant force in the Runet,” owning the most popular Websites in the former Soviet Union, including Russia, Ukraine, Kazakhstan, Georgia, and Armenia as well as others in the Czech Republic and Poland. By some estimates it reported “over 70 percent of all page views in the Russian-language Internet are on its companies’ Websites.”

 

 

From Wall Street Journal-

May 1, 2011

http://www.zdnet.com/blog/facebook/wsj-facebook-growth-exceeds-expectations-100-billion-valuation-justifiable/1306

Last month, a private-market transaction of 100,000 shares of Facebook Class B Common Stock priced at $32.00 apiece gave the website a valuation of $80 billion. Two months ago, Facebook was valued at $65 billion, when investment firm General Atlantic reportedly bought 0.1 percent of Facebook by purchasing roughly 2.5 million Facebook shares from former Facebook employees. Three months ago, Kleiner Perkins Caufield & Byers (KPCB) invested $38 million in Facebook, which was only worth 0.00073 percent of the social network, but still resulted in a valuation of $52 billion.

 

related-

http://techcrunch.com/2011/01/10/facebook-5/

 

Something is gotta give?

Go ahead and  Please. Buy Facebook Stock !

Do you feel lucky?

 

 

 

 

Analytics for Cyber Conflict

 

The emerging use of Analytics and Knowledge Discovery in Databases for Cyber Conflict and Trade Negotiations

 

The blog post is the first in series or articles on cyber conflict and the use of analytics for targeting in both offense and defense in conflict situations.

 

It covers knowledge discovery in four kinds of databases (so chosen because of perceived importance , sensitivity, criticality and functioning of the geopolitical economic system)-

  1. Databases on Unique Identity Identifiers- including next generation biometric databases connected to Government Initiatives and Banking, and current generation databases of identifiers like government issued documents made online
  2. Databases on financial details -This includes not only traditional financial service providers but also online databases with payment details collected by retail product selling corporates like Sony’s Playstation Network, Microsoft ‘s XBox and
  3. Databases on contact details – including those by offline businesses collecting marketing databases and contact details
  4. Databases on social behavior- primarily collected by online businesses like Facebook , and other social media platforms.

It examines the role of

  1. voluntary privacy safeguards and government regulations ,

  2. weak cryptographic security of databases,

  3. weakness in balancing marketing ( maximized data ) with privacy (minimized data)

  4. and lastly the role of ownership patterns in database owning corporates

A small distinction between cyber crime and cyber conflict is that while cyber crime focusses on stealing data, intellectual property and information  to primarily maximize economic gains

cyber conflict focuses on stealing information and also disrupt effective working of database backed systems in order to gain notional competitive advantages in economics as well as geo-politics. Cyber terrorism is basically cyber conflict by non-state agents or by designated terrorist states as defined by the regulations of the “target” entity. A cyber attack is an offensive action related to cyber-infrastructure (like the Stuxnet worm that disabled uranium enrichment centrifuges of Iran). Cyber attacks and cyber terrorism are out of scope of this paper, we will concentrate on cyber conflicts involving databases.

Some examples are given here-

Types of Knowledge Discovery in –

1) Databases on Unique Identifiers- including biometric databases.

Unique Identifiers or primary keys for identifying people are critical for any intensive knowledge discovery program. The unique identifier generated must be extremely secure , and not liable to reverse engineering of the cryptographic hash function.

For biometric databases, an interesting possibility could be determining the ethnic identity from biometric information, and also mapping relatives. Current biometric information that is collected is- fingerprint data, eyes iris data, facial data. A further feature could be adding in voice data as a part of biometric databases.

This is subject to obvious privacy safeguards.

For example, Google recently unveiled facial recognition to unlock Android 4.0 mobiles, only to find out that the security feature could easily be bypassed by using a photo of the owner.

 

 

Example of Biometric Databases

In Afghanistan more than 2 million Afghans have contributed iris, fingerprint, facial data to a biometric database. In India, 121 million people have already been enrolled in the largest biometric database in the world. More than half a million customers of the Tokyo Mitsubishi Bank are are already using biometric verification at ATMs.

Examples of Breached Online Databases

In 2011, Playstation Network by Sony (PSN) lost data of 77 million customers including personal information and credit card information. Additionally data of 24 million customers were lost by Sony’s Sony Online Entertainment. The websites of open source platforms like SourceForge, WineHQ and Kernel.org were also broken into 2011. Even retailers like McDonald and Walgreen reported database breaches.

 

The role of cyber conflict arises in the following cases-

  1. Databases are online for accessing and authentication by proper users. Databases can be breached remotely by non-owners ( or “perpetrators”) non with much lesser chance of intruder identification, detection and penalization by regulators, or law enforcers (or “protectors”) than offline modes of intellectual property theft.

  2. Databases are valuable to external agents (or “sponsors”) subsidizing ( with finance, technology, information, motivation) the perpetrators for intellectual property theft. Databases contain information that can be used to disrupt the functioning of a particular economy, corporation (or “ primary targets”) or for further chain or domino effects in accessing other data (or “secondary targets”)

  3. Loss of data is more expensive than enhanced cost of security to database owners

  4. Loss of data is more disruptive to people whose data is contained within the database (or “customers”)

So the role play for different people for these kind of databases consists of-

1) Customers- who are in the database

2) Owners -who own the database. They together form the primary and secondary targets.

3) Protectors- who help customers and owners secure the databases.

and

1) Sponsors- who benefit from the theft or disruption of the database

2) Perpetrators- who execute the actual theft and disruption in the database

The use of topic models and LDA is known for making data reduction on text, and the use of data visualization including tied to GPS based location data is well known for investigative purposes, but the increasing complexity of both data generation and the sophistication of machine learning driven data processing makes this an interesting area to watch.

 

 

The next article in this series will cover-

the kind of algorithms that are currently or being proposed for cyber conflict, the role of non state agents , and what precautions can knowledge discovery in databases practitioners employ to avoid breaches of security, ethics, and regulation.

Citations-

  1. Michael A. Vatis , CYBER ATTACKS DURING THE WAR ON TERRORISM: A PREDICTIVE ANALYSIS Dartmouth College (Institute for Security Technology Studies).
  2. From Data Mining to Knowledge Discovery in Databases Usama Fayyad, Gregory Piatetsky-Shapiro, and Padhraic Smyt

Indian Govt tries to censor Internet

Stupidity is contiguous  and Stupid Politicians are legion.

From-

http://online.wsj.com/article/SB10001424052970204542404577158342623999990.html?mod=WSJINDIA_hpp_LEFTTopStories

Google Inc. and Facebook Inc. are fighting back against increasing censorship demands from the Indian government and courts, arguing that they aren’t legally responsible for monitoring their websites and proactively removing user content that regulators deem objectionable.

The big threat for the companies at the moment is a lawsuit in a New Delhi trial court, which seeks to hold them and several other websites criminally liable for not censoring online content, including material that mocks or criticizes religious and political figures.

Read more: http://online.wsj.com/article/SB10001424052970204542404577158342623999990.html#ixzz1jVPdAsNT

————————————————————————————————————————————–

One not so apparent reason for Indian Govt to censor Internet is that the internet and social media were used for massive anti-Govt and anti-corruption protests in 2011. The Govt found itself on the backfoot, newspapers and television in India are generally considered pliable and manipulable by Govt  of  India (thanks to ad spends).Judiciary in India is also not known to be 100% honest or resistant of political pressures.

The incumbent Congress govt needs more legal weapons in its arsenal given elections are approaching this year in many states, and the need for more arrows in legal quivers  in India against the Internet is an inevitable and unfortunate next step. Since this is a global phenomenon (read- SOPA debate in US) ,and the huge huge internet population in India- this is one interesting battle to watch.

—————————————————————————————————————————————

Does Facebook deserve a 100 billion Valuation

some  questions in my Mind as I struggle to bet my money and pension savings on Facebook IPO

1) Revenue Mix- What percentage of revenues for Facebook come from Banner ads versus gaming partners like Zynga. How dependent is Facebook on Gaming partners. (Zynga has Google as an investor). What mix of revenue is dependent on privacy regulation countries like Europe vs countries like USA.

2) Do 800 million users of Facebook mean 100 billion valuation ? Thats a valuation of $125 in customer life time in terms of NPV . Since ad revenue is itself a percentage of actual good and services sold- how much worth of goods and services do consumers have to buy per capita , to give $125 worth of ads to FB. Eg . companies spend 5% of product cost on Facebook ads, so does that mean each FB account will hope to buy 2500$ worth of Goods from the Internet and from Facebook (assuming they also buy from Amazon etc)

3) Corporate Governance- Unlike Google, Facebook has faced troubling questions of ethics from the day it has started. This includes charges of intellectual property theft, but also non transparent FB stock option pricing in secondary markets before IPO, private placement by Wall Street Bankers like GoldMan Saachs, major investments by Russian Internet media corporations. (read- http://money.cnn.com/2011/01/03/technology/facebook_goldman/index.htm)

4) Retention of key employees post IPO- Key Employees at Google are actually ex- Microsofties. Key FB staff are ex-Google people. Where will the key -FB people go when bored and rich after IPO.

5) Does the macro Economic Condition justify the premium and Private Equity multiple of Facebook?

Will FB be the next Google (in terms of investor retruns) or will it be like Groupon. I suspect the answer  is- it depends on market discounting these assumptions while factoring in sentiment (as well as unloading of stock from large number of FB stock holders on week1).

Baby You Are a Rich Man. but not 100 billion rich. yet. Maybe 80 billion isnt that bad.

Quantitative Modeling for Arbitrage Positions in Ad KeyWords Internet Marketing

Assume you treat an ad keyword as an equity stock. There are slight differences in the cost for advertising for that keyword across various locations (Zurich vs Delhi) and various channels (Facebook vs Google) . You get revenue if your website ranks naturally in organic search for the keyword, and you have to pay costs for getting traffic to your website for that keyword.
An arbitrage position is defined as a riskless profit when cost of keyword is less than revenue from keyword. We take examples of Adsense  and Adwords primarily.
There are primarily two types of economic curves on the foundation of which commerce of the  internet  resides-
1) Cost Curve- Cost of Advertising to drive traffic into the website  (Google Adwords, Twitter Ads, Facebook , LinkedIn ads)
2) Revenue Curve – Revenue from ads clicked by the incoming traffic on website (like Adsense, LinkAds, Banner Ads, Ad Sharing Programs , In Game Ads)
The cost and revenue curves are primarily dependent on two things
1) Type of KeyWord-Also subdependent on
a) Location of Prospective Customer, and
b) Net Present Value of Good and Service to be eventually purchased
For example , keyword for targeting sales of enterprise “business intelligence software” should ideally be costing say X times as much as keywords for “flower shop for birthdays” where X is the multiple of the expected payoffs from sales of business intelligence software divided by expected payoff from sales of flowers (say in Location, Daytona Beach ,Florida or Austin, Texas)
2) Traffic Volume – Also sub-dependent on Time Series and
a) Seasonality -Annual Shoppping Cycle
b) Cyclicality– Macro economic shifts in time series
The cost and revenue curves are not linear and ideally should be continuous in a definitive exponential or polynomial manner, but in actual reality they may have sharp inflections , due to location, time, as well as web traffic volume thresholds
Type of Keyword – For example ,keywords for targeting sales for Eminem Albums may shoot up in a non linear manner after the musician dies.
The third and not so publicly known component of both the cost and revenue curves is factoring in internet industry dynamics , including relative market share of internet advertising platforms, as well as percentage splits between content creator and ad providing platforms.
For example, based on internet advertising spend, people belive that the internet advertising is currently heading for a duo-poly with Google and Facebook are the top two players, while Microsoft/Skype/Yahoo and LinkedIn/Twitter offer niche options, but primarily depend on price setting from Google/Bing/Facebook.
It is difficut to quantify  the elasticity and efficiency of market curves as most literature and research on this is by in-house corporate teams , or advisors or mentors or consultants to the primary leaders in a kind of incesteous fraternal hold on public academic research on this.
It is recommended that-
1) a balance be found in the need for corporate secrecy to protest shareholder value /stakeholder value maximization versus the need for data liberation for innovation and grow the internet ad pie faster-
2) Cost and Revenue Curves between different keywords, time,location, service providers, be studied by quants for hedging inetrent ad inventory or /and choose arbitrage positions This kind of analysis is done for groups of stocks and commodities in the financial world, but as commerce grows on the internet this may need more specific and independent quants.
3) attention be made to how cost and revenue curves mature as per level of sophistication of underlying economy like Brazil, Russia, China, Korea, US, Sweden may be in different stages of internet ad market evolution.
For example-
A study in cost and revenue curves for certain keywords across domains across various ad providers across various locations from 2003-2008 can help academia and research (much more than top ten lists of popular terms like non quantitative reports) as well as ensure that current algorithmic wightings are not inadvertently given away.
Part 2- of this series will explore the ways to create third party re-sellers of keywords and measuring impacts of search and ad engine optimization based on keywords.

Does the Internet need its own version of credit bureaus

Data Miners love data. The more data they have the better model they can build. Consumers do not love data so much and find sharing data generally a cumbersome task. They need to be incentivize for filling out survey forms , and for signing to loyalty programs. Lawyers, and privacy advocates love to use examples of improper data collection and usage as the harbinger of an ominous scenario. George Orwell’s 1984 never “mentioned” anything about Big Brother trying to sell you one more loan, credit card or product.

Data generated by customers is now growing without their needing to fill out forms and surveys. This data is about their preferences , tastes and choices and is growing in size and depth because it is generated from social media channels on the Internet.It is this data that can be and is captured by social media analytics.

Mobile data is also growing, including usage of location based applications and usage of Internet from the mobile phone is leading to further increases in data about consumers.Increasingly , location based applications help to provide a much more relevant context to the data generated. Just mobile data is expected to grow to 15 exabytes by 2015.

People want to have more and more conversations online publicly , share pictures , activity and interact with a large number of people whom  they have never met. But resent that information being used or abused without their knowledge.

Also the Internet is increasingly being consolidated into a few players like Microsoft, Amazon, Google  and Facebook, who are unable to agree on agreements to share that data between themselves. Interestingly you can use Yahoo as a data middleman between Google and Facebook.

At the same time, more and more purchases are being done online by customers and Internet advertising has grown much above the rate of growth of other mediums of communication.
Internet retail sales have the advantage that better demand predictability can lead to lower inventories as retailers need not stock up displays to look good. An Amazon warehouse need not keep material to simply stock up it shelves like a K-Mart does.

Our Hypothesis – An Analogy with how Financial Data Marketing is managed offline

  1. Financial information regarding spending and saving is much more sensitive yet the presence of credit bureaus alleviates these concerns.
  2. Credit bureaus collect information from all sources, aggregate and anonymize the individual components accordingly.They use SSN as a unique identifier.
  3. The Internet has a unique number too , called the Internet Protocol Address (I.P) 
  4. Should there be a unique identifier like Internet Security Number for the Internet to ensure adequate balance between the need for privacy as well as the need for appropriate targeting? 

After all, no one complains about privacy intrusions if their credit bureau data is aggregated , rolled up, and anonymized and turned into a propensity model for sending them direct mailers.

Advertising using Social Media and Internet

https://www.facebook.com/about/ads/#stories

1. A business creates an ad
Let’s say a gym opens in your neighborhood. The owner creates an ad to get people to come in for a free workout.
2. Facebook gets paid to deliver the ad
The owner sends the ad to Facebook and describes who should see it: people who live nearby and like running.
The right people see the ad
3. Facebook only shows you the ad if you live in town and like to run. That’s how advertisers reach you without knowing who you are.

Adding in credit bureau data and legislative regulation for anonymizing  and handling privacy data can expand the internet selling market, which is much more efficient from a supply chain perspective than the offline display and shop models.

Privacy Regulations on Marketing using Internet data
Should laws on opt out and do not mail, do not call, lists be extended to do not show ads , do not collect information on social media. In the offline world, you can choose to be part of direct marketing or opt out of direct marketing by enrolling yourself in various do not solicit lists. On the internet the only option from advertisements is to use the Adblock plugin if you are Google Chrome or Firefox browser user. Even Facebook gives you many more ads than you need to see.

One reason for so many ads on the Internet is lack of central anonymize data repositories for giving high quality data to these marketing companies.Software that can be used for social media analytics is already available off the shelf.

The growth of the Internet has helped carved out a big industry for Internet web analytics so it is a matter of time before social media analytics becomes a multi billion dollar business as well. What new developments would be unleashed in this brave new world is just a matter of time, and of course of the social media data!