ht MacRumors for the transcript

No matter how long you stand out in the pouring rain, waiting in line to get tickets to your favorite show, you don’t stand a chance against the bots.

From the New York Times:

“Bots,” computer programs used by scalpers, are a hidden part of a miserable ritual that plays out online nearly every week in which tickets to hot shows seem to vanish instantly.

Long a mere nuisance to the live music industry, these cheap and widely available programs are now perhaps its most reviled foe, frustrating fans and feeding a multibillion-dollar secondary market for tickets.

According to Ticketmaster, bots have been used to buy more than 60 percent of the most desirable tickets for some shows; in a recent lawsuit, the company accused one group of scalpers of using bots to request up to 200,000 tickets a day.

Ticketmaster and its parent company, Live Nation Entertainment, have stepped up efforts to combat bots, in part to improve the ticket-buying experience for concertgoers, but also to burnish the company’s reputation with consumers. The result has been a game of cat and mouse between the company and the bots.

“As with hackers, you can solve it today, and they’re rewriting code tomorrow,” said Michael Rapino, Live Nation’s chief executive. “Thus the arms race.”

And yes, 98% of the reason I posted this was for the headline.

If you rotate the CBS logo to the side you’ll find it’s actually Pac-Man! Well, at least in the active imagination of Nikkolas Smith.

ht BuzzFeed

These graphs are all from Google Trends. Each graph shows interest over time in social networks.

Facebook

Instagram

Google+

Pinterest

Twitter

LinkedIn

MySpace

App.net

Flickr

Reddit

Digg

Friendster

Here’s where MySpace passed the torch to Facebook. And Facebook’s explosive growth since.

Here’s Flickr versus Instagram versus Pinterest.

Here’s Reddit overtaking Digg.

The Internet just got a museum of Internet history.

And in it, you’ll find artifacts and files related to the earliest days of the world wide web. Like the screenshot you see above: the first website created and the de facto birth certificate of the Internet.

British physicist Tim Berners-Lee invented the web at CERN in 1989. The project, which Berners-Lee named “World Wide Web”, was originally conceived and developed to meet the demand for information sharing between physicists in universities and institutes around the world.

The first website at CERN – and in the world – was dedicated to the World Wide Web project itself and was hosted on Berners-Lee’s NeXT computer. The website described the basic features of the web; how to access other people’s documents and how to set up your own server. Although the NeXT machine – the original web server – is still at CERN, sadly the world’s first website is no longer online at its original address.

To mark the anniversary of the publication of the document that made web technology free for everyone to use, CERN is starting a project to restore the first website and to preserve the digital assets that are associated with the birth of the web.

Learn more about the project here.

Or, check out the Museum of Endangered Sounds.

It’s no secret that big brands are trying to use big data to make big money on your every little move online. But now technology is allowing advertisers to bridge the gap between desktop and mobile, and they’re only just getting started.

I’ll just leave the following headline from the Wall Street Journal here without further comment: “Online Ads Can Now Follow You Home“.

Advertisers already know what people are up to on their personal computers. But understanding their online whereabouts on smartphones or tablets has remained elusive.

A number of companies are trying to better pinpoint mobile users’ online activity with new software and techniques they say could help advertisers track users across devices.

By harvesting cross-screen identities, the ad industry could serve ads to mobile phones based on the interests people express when surfing the Web on their PCs. - Wall Street Journal

This photo illustration says just about everything there is to say about society’s ability to conserve resources.

The Reddit thread this came from is definitely worth checking out.

The numbers don’t lie. But sometimes they tell you a different version of the truth.

Big data is bringing change to conventional wisdom everywhere. But sometimes the knowledge inferred from data is flawed or compromised. The true usefulness of big data (aside from being everyone’s favorite buzzword) is open to interpretation, and highly related to whomever is doing the interpreting.

There’s an art to interpreting mathematics.

[W]hile Big Data technology has indeed increased our ability to store and process lots of disparate data in real-time, the technology is only as useful the people managing it. - ReadWriteWeb

Where there are humans, there will be errors.

[M]ore human/data interaction means a lot more room for error (and inefficiency) around increasingly critical data sets — which, as we’ve seen, can have very serious results. Which means that, if we want to make the reality of Big Data match the dream, we need to spend serious time around providing usability that guides human users in the best way to engage with the data, and automation that takes human interaction (and human error) out of the picture for a lot of the basic calculations and tasks — and for some of the complicated ones, too. - All Things D

Big data will always be biased because humans set up the experiments.

Data and data sets are not objective; they are creations of human design. We give numbers their voice, draw inferences from them, and define their meaning through our interpretations. Hidden biases in both the collection and analysis stages present considerable risks, and are as important to the big-data equation as the numbers themselves. - HBR

Data scientists: think like sociologists.

In the near term, data scientists should take a page from social scientists, who have a long history of asking where the data they’re working with comes from, what methods were used to gather and analyze it, and what cognitive biases they might bring to its interpretation - HBR

Bad data leads to bad decisions.

“The greatest enemy of knowledge is not ignorance, it is the illusion of knowledge.” – Stephen Hawking

Dirty data reportedly costs U.S. businesses $600 billion annually.

If you’ve ever wondered who the Internet is wondering about wonder no longer. Here’s an A-to-Z guide of the most inquired-about people on the Internet according to Google’s Search Suggest. (Some queries take more creative liberty than others.)

A

B

C

D

E

F

G

H

I

J

K

L

M

N

O

P

Q

R

S

T

U

V

W

X

Y

Z

If you want to get a glimpse into the collective mentality of the human race, consult a search engine. Search engine query data (i.e. what people type into search engines) serves as a real-time indicator of the demand for information across a variety of subject matter. What people are searching for is tightly tied to societal and economic trends.

As an example, if you start typing into the Google search bar and the search engine will offer up suggestions based on what others are searching (like you’ll see in the image below).

The results can sometimes be hilarious and sometimes be horrifying but are almost always interesting.

But if you dive deeper, you’ll find that there’s more interesting and meaningful data available. For instance, here’s a Google Trends graph displaying interest in “gas mileage” over the past nine years. You’ll see a surge in interest in the summer of 2008, when gas prices were at a record high.

It makes sense, of course. As prices for gas surged, more people were interested in getting better gas mileage. This is the type of logic researchers are applying to the world of finance. It all centers around understanding how human interest can be correlated with larger market trends.

Recently, researchers have found that the treasure trove of data found in Google’s search trends can predict ebbs and flows in the stock market.

From BBC: The volume of Google searches for finance-related terms may predict moves in markets, research suggests.

As the search volume on generic terms such as “debt”, “portfolio” and “stocks” fell, the Dow Jones average tended to go up – and vice versa.

An investment strategy based on these search volume data between 2004 and 2011 would have made a profit of 326%. [A] new report (Quantifying Trading Behavior in Financial Markets Using Google Trends) gives hints that straightforward analysis of interest in general finance-related terms can be a good predictor of overall market health.

“We were intrigued by the idea that stock market data serves as a really large record of all the actions people take in the stock market, but don’t necessarily tell us much about how people decided to take those actions,” said Suzy Moat of University College London, co-author of the paper.

“We wondered whether by looking at Google, we could get some insight into some early information-gathering stages of how people make decisions,” she told BBC News.

The team started with a set of 98 search terms and tracked how search volumes on those terms varied over a period between 2004 and 2011, and correlated those with the Dow Jones Industrial Average.

Generally, searches for the most finance-focused terms such as “stocks” and “revenue” went down before rises in that market average, whereas when those terms were searched for more often, the average tended to fall in subsequent weeks.

The team developed a hypothetical investment strategy through the period, buying notional stocks in weeks that financial-term search volume fell, and selling them when volume rose – a strategy that would have gained them a profit of 326%. By comparison, simply buying in 2004 and selling in 2011 would have yielded a profit of 16%

Google serves results for about 100 billion searches per month. We’re only starting to understand what we can learn about society and the economy along the way.