Getting ratioed for your bad take

Technology is a constant source of new vocabulary – not just new words but new ways of using existing words. One I’ve noticed this year is ratio as a verb in internet slang, which I’ve bundled here with the more familiar take as a noun.

Ratio entered English in the 16thC as a noun borrowed from Latin, gaining its familiar modern sense decades later in a translation of Euclid. About a century ago – the OED’s first citation is from 1928 – ratio began life as a verb meaning ‘express as a ratio’ or similar. Here’s an example from Harold Smith’s book Aerial Photographs (1943):

Each print which departs from the average scale or shows any apparent tilt is rectified and ‘ratioed’, or corrected for scale, by means of a projection printer.

And now a new sense of ratio as a verb is emerging on Twitter.

Source: Getting ratioed for your bad take | Sentence first

Here’s an intro, if needed, to the use of ‘ratio’ on Twitter to describe Tweets which have disproportionately more replies than retweets or likes. And, after that, an exploration of how it is used as a verb.

See also nominalisation: how verbs become ‘zombie’ nouns. I’m particularly bothered by two (at the moment): ‘bake’, as in the thing that was baked; and ‘ask’, as in the instruction.

The UX of 280

This is where the design challenge comes in: How can we make a UI that communicates these different character constraints that is still easily understood globally? Simply replacing the number doesn’t work because we can’t be certain which language you’re going to be Tweeting in. We could guess which language you’ll use, based on your location or system language, but that falls apart quickly, as many people live in foreign countries or travel regularly. Additionally, many people Tweet in multiple languages, sometimes within a single Tweet. Because we count dense alphabets differently than non-dense, mixed language Tweets can result in some intricate math that we want to be able to abstract away. The challenge here was to create a design that adapts to different character limits without relying on a number, works with the many ways people compose Tweets, and is intuitive enough that people don’t have to spend time thinking about it.

A recap of some of the UX decisions made to show that Twitter users now have 280 characters with which to compose their post, not 140.

Source: Looking After Number One-forty – Twitter Design & Research – Medium

Fuck Twitter

Gabe Weatherhead:

Twitter-the-company is made up of people that have consistently made poor, self-serving decisions while patting themselves on the back for making the world more connected. Well, fuck you all. You gluttons helped put us all here and now suddenly you might come up with an algorithm to spot Nazis? Here’s an idea to put on your fucking Trello board: Look for avatars with swastikas on day one. Take day two off to recover from your code bash hangover. Day three you can look for accounts that mostly post negative sentiments (using a Python library that’s five years old) and then map their connections to find another Nazi pool-party. On day four, take another break. You deserve a rest for waiting a decade to do even the smallest amount of engineering work to make the world better. Day five might be busy while your executives are explaining to congress why you actively assisted a foreign government to spread disinformation during the US election. Also, enjoy your weekend you fucks.

Source: Fuck Twitter

Some opinions about Tweetstorms

I will say: though tweet storms suck as a medium for collecting and preserving your ideas, they are a pretty great compositional gambit. One of the biggest struggles I have as a writing instructor is getting people to just write. By breaking that down to 140 char chunks, I think people find it easier to piece together something that flows one idea at a time.

So please! Keep doing that. Just, you know, copy the words and paste them into something with a single stable URL.

Tim Maly: Some Opinions about Tweetstorms

Domino’s Instagram Is Gross

The Domino’s feed is not appetizing by any objective measure. But if you look at it long enough, over enough time, the cadence of grotesqueness begins to sink in. The studio lighting and Photoshop-enhanced pepperoni of Papa John’s and Pizza Hut start to look like the culinary equivalent of a French manicure and a spray tan. Fake.

Instead of employing professional photographers, Domino’s relies on its digital marketing team to update the social media feeds. The cinema verité approach began in 2012, when Domino’s launched the Show Us Your Pizza Campaign, and shared the (often ugly) food photos taken by its customers. After that, the aesthetic just stuck. And today, the pizzas Domino’s photographs are all real, either pulled from a test kitchen oven, or delivered by an employee, no food stylist required. And, clearly, there’s no sweating the need for natural light or perfect post-processing by Domino’s employees who will sometimes even take photographs in their own suburban homes. Domino’s is a living embodiment of a #nofilter brand.

Domino’s Instagram Is Gross. That’s By Design

Tagging fake news on Facebook doesn’t work

Jason Schwartz for Politico:

Facebook touts its partnership with outside fact-checkers as a key prong in its fight against fake news, but a major new Yale University study finds that fact-checking and then tagging inaccurate news stories on social media doesn’t work.

The study, reported for the first time by POLITICO, found that tagging false news stories as “disputed by third party fact-checkers” has only a small impact on whether readers perceive their headlines as true. Overall, the existence of “disputed” tags made participants just 3.7 percentage points more likely to correctly judge headlines as false, the study said.

This is particularly disappointing:

The researchers also found that, for some groups—particularly, Trump supporters and adults under 26—flagging bogus stories could actually end up increasing the likelihood that users will believe fake news.

You are the product

John Lanchester:

What this means is that even more than it is in the advertising business, Facebook is in the surveillance business. Facebook, in fact, is the biggest surveillance-based enterprise in the history of mankind. It knows far, far more about you than the most intrusive government has ever known about its citizens. It’s amazing that people haven’t really understood this about the company. I’ve spent time thinking about Facebook, and the thing I keep coming back to is that its users don’t realise what it is the company does. What Facebook does is watch you, and then use what it knows about you and your behaviour to sell ads. I’m not sure there has ever been a more complete disconnect between what a company says it does – ‘connect’, ‘build communities’ – and the commercial reality. Note that the company’s knowledge about its users isn’t used merely to target ads but to shape the flow of news to them. Since there is so much content posted on the site, the algorithms used to filter and direct that content are the thing that determines what you see: people think their news feed is largely to do with their friends and interests, and it sort of is, with the crucial proviso that it is their friends and interests as mediated by the commercial interests of Facebook. Your eyes are directed towards the place where they are most valuable for Facebook.

I finally got round to reading this—I currently, and temporarily, have a lot of free time on my hands, so I’m reading everything—and it’s fantastic. Recommended reading for anyone interested in the nascent subject of web platforms (in fact this piece is reminiscent at times of John Herrman, who is currently the writer of the most interesting and relevant articles on the topic).

Data reporting links from NICAR17

Chrys Wu has a comprehensive list of talks and resources from NICAR17—the conference for the (U.S.) National Institute for Computer-Assisted Reporting.

Some that jumped out at me as being particularly useful and/or interesting:

Photoshop yourself into a celebrity’s Instagram feed

Until a few minutes ago, I didn’t know who Kendall Jenner was, but it appears she’s a Kardashian clan celebrity. Superfan Kirby Jenner (which may not be his real name) runs an Instagram account where he Photoshops himself into Kendall’s pictures, and it’s absolutely hilarious:

 

Confessions of an Instagram Influencer

On Bloomberg, Max Chafkin (with a little help from a couple of agencies) turns himself into one of those horrible Instagram lifestyle/fashion brand-human-hybrids:

A week later, after a haircut the price and duration of which I refuse to share, I met Marcel Floruss and Nathan McCallum, two of Socialyte’s professional clients, at Lord & Taylor to borrow some outfits. The two men are opposites in almost every way. McCallum is compact and favors ripped jeans and piercings, and Floruss is lanky and clean-cut. Both are cartoonishly handsome, and both (I noticed this later when I checked out their Instagram work) have amazing abdominal muscles. “Constantly,” Floruss said, when I asked him how often he takes pictures of himself. “You sell part of your soul. Because no matter what beautiful moment you enjoy in your life, you’re going to want to take a photo and share it. Distinguishing between when is it my life and when am I creating content is a really big burden.”

[…]

By dinnertime, I’d posted a second picture and had acquired a few dozen likes and roughly three followers. That’s actually not bad for somebody with an almost nonexistent presence on Instagram, but it was discouraging to me, because I would need at least 5,000 followers to have any hope of making money. That night, I signed up for a service recommended to me by Socialyte called Instagress. It’s one of several bots that, for a fee, will take the hard work out of attracting followers on Instagram. For $10 every 30 days, Instagress would zip around the service on my behalf, liking and commenting on any post that contained hashtags I specified. (I also provided the bot a list of hashtags to avoid, to minimize the chances I would like pornography or spam.) I also wrote several dozen canned comments—including “Wow!” “Pretty awesome,” “This is everything,” and, naturally, “[Clapping Hands emoji]”—which the bot deployed more or less at random. In a typical day, I (or “I”) would leave 900 likes and 240 comments. By the end of the month, I liked 28,503 posts and commented 7,171 times.