Sometimes I wish I could use Twitter as a different account, and read all the conversations and references that result from the unique list of accounts a person has chosen to follow (sometimes over the course of years!) There’s no “use Twitter as @x” mode yet, so the next best thing is to create a list of all the accounts somebody follows. The public view of that list is, roughly, that person’s main timeline. This came in handy recently as I was trying to follow a basketball game, because I don’t yet follow the kinds of people who make insightful comments about basketball games.1
Fortunately, this is a one-liner with the super handy command line program t. If you don’t have t installed, I strongly recommend downloading and configuring it even if you don’t want to do the rest of these steps. It’s just a very useful tool to have in your Twitter toolbelt.
Here’s the t command:
t followings OTHERACCOUNT | xargs t list add LISTNAME
Where OTHERACCOUNT should be replaced with the name of the account you want to use, and the LISTNAME should be the name of an existing list. I just made the list through the web interface, which allows me to set it as private.
Important note! If you don’t set it as private, or if you ever make it public, members of the list will get a notification that they’ve been added. People tend to think that’s very weird.
I also like to add the originating account to the list so you can see replies to and from that person.
Finally, some caveats: obviously, you won’t get access to private accounts that user follows. You will see people that user has muted, unless you’ve already muted them. You won’t see notifications that user sees. And of course, if the user has something like Tweetdeck and uses columns other than the “main timeline,” you won’t know. Still, it’s a pretty good way to check out Twitter from somebody else’s point of view.
You could argue it’s like a DIY version of Twitter Moments, where you trust the curation done by an individual user is better than the algorithm, but I won’t be the one making that argument. ↩
I’ve unleashed a new bot onto the Twitter timeline today: @pomological tweets an image and description from the Pomological Watercolor Collection in the USDA’s National Agricultural Library. (As all of my friends and anybody unfortunate to stand near me at parties knows, I’ve worked extensively on bringing these watercolors to the public.) These are beautiful images with serious historical significance, so it’s fun to slip them in between everything else happening on Twitter. You should follow! Here’s the first automated tweet from the account:
For the nerd stuff: the code (such as it is) is available on Github. The actual bot doesn’t do much; the trick was getting all the data together in advance so it just has to wake up every three hours and pick from about 7500 statuses to post. One thing that has been super helpful on a lot of these projects is a scrape that Dave Riordan did earlier this year of the Collection’s page on the USDA site.
On the programming side, I continue to be incredibly pleased with the book Automate the Boring Stuff With Python. I feel like I promote it too much, but it really has been so helpful and has gotten me off the ground on a bunch of projects that I was too intimidated to face before. In this project, the manipulation of CSVs and scraping web pages with BeautifulSoup were done with skills straight out of the book.
The New York Times has reported that, despite the long-standing traditional meaning of the name, people named Isis have faced issues ranging from inconveniences to major discrimination in the past several years on the basis of their name. This problem disproportionately affects women, and it’s not just a few cases. What is the scale of people who may be affected?
Using data from the Social Security Administration about birth names and death rates, I estimate that there are 10,620 living people designated female at birth named Isis in the US.1 The full data is available on Github.
SSA only makes information on names outside of the top 1000 available as raw data, not through its “explorer.” I downloaded it and pulled the number of Isis births into one document. That would be sufficient to calculate (roughly) the number of people born with the name Isis, but wouldn’t account for how many of these people may no longer be living.
In order to determine that number, I went to another data set from SSA: the Actuarial Life Table.2 That dataset includes the percentage of living people at each given age, as of 2011. I fudged the numbers forward, and treated is as if it were 2014 data.
That was the most complex component of the estimation, and it turned out to be mostly unnecessary. The name Isis did not appear to be given to even five babies designated male or female at birth in a single year between 1901 and 1960,3 so nearly all US-born people named Isis are under 55 years old and are still alive.
Put in concrete terms: of the estimated 10,715 people in the US named Isis at birth, only about 95 are deceased.
There are serious limitations to this estimation. Among them: it doesn’t account for people born outside of the United States or who did not apply for Social Security cards. The survivorship calculation assumes that the name is evenly distributed across demographics, which is probably not true. It depends on SSA’s treatment of gender as an unchanging binary, which is incorrect. Still, it does provide some insight into the number of people affected by heavy-handed efforts by social media platforms and others to filter out content related to Daesh.
There is no comparable data for people designated male at birth because the name has not been common enough to be reported.
Obviously, not all people designated female at birth are women, and not all women are designated female at birth. Further, not all people with the name Isis will have it at birth, and not all people born with it will have it as a name now. This is a major limitation of the data. I’ve tried to be as precise as possible throughout this post, but please feel free to suggest corrections. ↩
The incredible botmaster Darius Kazemi has a popular GitHub repository called “corpora“, which contains, well, collections of all kinds of things. It can be really handy to have access to a list of words that all fall into a certain group, and so Kazemi makes it available completely freely and with a CC0 waiver to place it in the public domain.
Earlier this week I landed my first contribution to corpora: a collection of 1000 apple varieties, or cultivars, picked from the metadata of the USDA Pomological Watercolor collection. They are roughly the 1000 that appear most frequently out of 1500 or so, though a good chunk (200 or so of this corpus, and 700 or so of the overall collection) appear only once.
The names on the list are charming and goofy, and I hope they are useful to somebody. Here are some of my favorite cultivars:
Newtown Spitzenburg
Shiawassee
White Winter Pearmain
Royal Limbertwig
Sops of Wine
Peasgood Nonesuch
Limber Limb Pippin
Hollandberry Admirable
Petite Douce Rousse
Russian Gravenstein
Sweet Nonesuch
It feels like a good way of extending the pomological watercolor work I’ve been doing into a community of artists and botmakers I’d like to support.
Over on Techdirt, I wrote a short piece about how uncertainty surrounding ridiculously long copyright terms is likely keeping newspapers from the 1920s onward out of major archives. We’re very likely in the midst of a sea change in journalism, but future generations may not be able to study what we’re producing and exploring as likely business shifts make the copyright question even thornier. From the article:
In the world of media journalism, we talk a lot about the future. But we can’t have a coherent conversation about that without thinking about the past and the present. And those thoughts, in turn, rely on access to the history that we’ve allowed to be locked up under effectively unlimited copyright restrictions or as orphan works.
Per usual, the comments over at Techdirt, too, have been lively.