Content drift is how a piece of linked content may change over time such that what was once linked may in fact now be very different.

An article you link to may be corrected, opinions may be changed, content may be expanded or removed, a paywall may be put in the way, and yet the link keeps steadfastly pointing. Content drift, along with link rot — pages being lost — is one of the ways that the information we have come to rely on on the internet can be gradually undermined.

Here's a thread on content drift and link rot from Harvard's Jonathan Zittrain — at least, it was a thread on it when I wrote this. Also see: The Internet is Rotting.

Content drift

Where is the Life we have lost in living?
Where is the wisdom we have lost in knowledge?
Where is the knowledge we have lost in information?

In these short lines he touched on an intriguing differentiation between wisdom, knowledge and information. Add data at the beginning and people have proposed various models such as a hierarchy, chain, or pyramid to help understand their relationships.

It's perhaps like a skier who's experienced the data points of snow and conditions in the mountains most days of their life. They begin to observe predictable patterns and then gradually distill these into knowledge of the relationships between the weather and avalanche risk. And over a lifetime of building and applying such knowledge they may develop instincts and behaviours for predicting risk and living with the mountains that may even be called wisdom.

In an unlikely scenario, I once wrote a class paper, that somehow people still find to read, on The Data, Information, Knowledge, Wisdom Chain: The Metaphorical link (pdf). It touches on some of the subtle differences of how we think about each of these, such as how I might complain of information overload, but never of knowledge overload.

Data Information Knowledge Wisdom

Also known as Brandolini’s Law, this is the simple observation that it’s far easier to produce and spread BS, misinformation and nonsense than it is to refute it.

In fact, the iSchool at University of Washington launched a course on Calling Bullshit with the first class being booked up within one-minute of registration. The professors, Carl Bergstrom and Jevin West have since written a book: Calling Bullshit: The Art of Skepticism in a Data-Driven World.

Phil Williamson in Nature also wrote a nice article emphasizing that we should take the time and effort to correct misinformation where we can. In it, he proposed the idea that “The global scientific community could…set up its own, moderated, rating system for websites that claim to report on science. We could call it the Scientific Honesty and Integrity Tracker, and give online nonsense the SHAIT rating it deserves.”

The BS asymmetry principle

The data prison is the default table where the bars of the cells crowd out your data with non-data-ink and lock your data into a prison.

When data is locked into cells, their relationships with other data are harder to see, and the reader has to work harder to learn from your table.

When making a table of data, consider if, by adjusting spacing and alignment, you can make the columns or rows clear enough as they are without the prison bars. And look for opportunities to remove the visual noise to let your data and the relationships between them shine out.

The data prison is a concept from Edward Tufte. Check out the beautiful Envisioning Information

The data prison

The Streisand effect describes what happens when you try to censor or suppress information: it draws attention to it.

It’s named Streisand after Barbara Streisand tried to have an aerial photo of her house removed from a set of California coastline photos intended to document coastal erosion. Views of the photo apparently went from 6 to 420,000.

The Streisand Effect as a term was coined by Mike Masnick

Information sketches