Incumbents almost always prevail with sustaining innovation

In disruptive circumstances, the entrants are likely to beat the incumbents

H/t Clayton Christensen, The Innovator’s Solution

Two years ago on a weekend, I built a tool to make it easier to evaluate Twitter accounts. Since then 36590 people used it to analyze 55390 different Twitter accounts.

Over the last months @mmkaradeniz and I made a new version. We launched it last night:

@accountanalysis doesn't use machine learning or AI „magic“. Instead of telling users if an account is authentic or not, it helps them to evaluate the accounts themselves.

It visualizes the different features (date, time, type, app, etc.) of Tweets to make them interpretable. /1

@accountanalysis The core concept is still the same, but it looks much better and is easier to operate. Not only for the users, but us developers as well. Enabling us to continuously roll out new features in the future.

Side-by-side screenshots of the old and new version. /2

@accountanalysis Some people had questions what different charts displayed and how they could be interpreted. There are now explanations for all charts, that can be toggled on and off at any time. /3

@accountanalysis The selected/retrieved Tweet count moved to the top to make it clear that not all Tweets of an account are analyzed. 3200 is the API limit by Twitter. It's possible to get more through the Premium API, but I don't think people would pay $100+ for the analysis of one account. /4

There are only 3 real challenges to free speech at the tech level a) the need for CDNs at scale b) distortions of net neutrality c) nation-state firewalls.

All other claims are about social governance on private sites, where applying free speech doctrine is a stretch at best

Baseline free speech has expanded modestly since 1776 or whatever your reference point is.

About 20% beyond core case of government-critical speech in formally public spaces and media I'd say.

You can (for example) have more kinds of conversations in a bar without getting punched or kicked out by other patrons or management. You can teach more things in colleges than 100 years ago.

That's a genuine de facto increase in practical, informal free speech/expression.

But online free "speech" is, as @reneed has pointed out, not an expansion of free speech at all, but an expansion of reach. It is like the secondary freedom of the press to print as many copies as they can afford, not primary freedom to say what they want

If a newspaper wants to print more copies it has buy more printing equipment and front more upfront costs. Reach for a paper publication is not "free", but a function of desired circulation, which in turn is a function of demand prediction etc. But that logic is obscured online.

Today we’re going to talk statistics and semantics. I’m going to do so via the medium of one of Twitter’s favourite stats 1.7% (aka “intersex is as common as red heads.”). The statistic comes from Fausto-Sterling’s work, which you can see a review of

To understand the statistic, we have to drill down into what the meaning of the word intersex is. Most people think of hermaphrodites. Let’s stress at the beginning there has been no example ever of a human being with both sets of working reproductive organs.

There is a very tiny percentage of people who are born with both testicular and ovarian tissue (what Fausto-Sterling calls “true hermaphrodites”). And when I say tiny, we’re talking intersex tiny, as in 0.0117/1,000 live births, or one in 1,000,000. These are very complex cases.

So, how do we arrive at 1.7%? What Fausto-Sterling does is apply a wider scope. Instead, she argues that intersex is anything that doesn’t fit into a platonic ideal. The platonic ideal, in this case, being a phenotypically perfect XX female and a phenotypically perfect XY male.

This means that intersex, used in the context of the 1.7% statistic, includes people for whom there is no sexual ambiguity. Take for example Klinefelter syndrome (XXY males). As Fausto-Sterling acknowledges, many males can go their whole life and not realise they are XXY.

Setting up Git/GitHub to work with R/RStudio can be challenging.

Here's a thread to walk you through the process step by step.

This thread also lives in blog post form. #rstats

Preamble: the best overall resource, and one I rely on extensively, is Happy Git with R by @JennyBryan and @jimhester_.

If you have additional questions about Git/GitHub + R/RStudio, this should be your first

The first step to set things up is to install Git.

Chapter 6 of Happy Git with R lays out the process for Mac, Windows, and Linux users.

I’m on a Mac so Git came pre-installed on my computer. I was able to verify that I had Git installed using the terminal in RStudio.

The next step is to configure Git.

This is covered in Chapter 7 of Happy Git with R , though I show what I believe to be a slightly easier process using the edit_git_config() function from the {usethis} package.