October 8, 2016
botwars, grammar privilege, happy mutants, humor, language, muphry's law, Post, twitter, Uncategorized, xenophobia
Muphry’s Law predicts that “if you write anything criticising editing or proofreading, there will be a fault in what you have written.”
Buzzfeed’s Your In America account watches the Twitter firehose for people posting some variation on “Speak English your in America!!” and corrects their your/you’re usage, with a sarcastic quip. It’s solid fucking gold.
report this ad I’m the “Honourary Steward” …
Doges are done; sneks are so September. What’s next? @Hay_Man’s Peasant Memes!
The only good meme pic.twitter.com/2FyEePROxU — hay man (@hay__man) October 3, 2016
(via Laurie Penny)
report this ad JWZ has discovered the greatest Google Image search. READ THE REST If you want to let people know you care but don’t have time to send thoughts and prayers every time there’s …
See sample pages from this book at Wink.
The Pet Dragon
by Christopher Niemann
2008, 40 pages, 9 x 11.8 x 0.4 inches (hardcover)
$16 Buy a copy on Amazon Chinese characters are wonderfully expressive, straddling the fine line between the written word and illustration. Esteemed graphic designer and picture book creator Christoph Niemann realized as much …
My father had just one question when he started reading my first novel earlier this year: “Who taught you to speak like that?”Written in Singlish—the folksy patois of Singapore that combines English, Mandarin, Malay and Chinese dialects including Teochew and Hokkien—Sarong Party Girls has a … Continue reading Source link
August 11, 2016
#makeDonaldDrumpfAgain, adversarial stylometry, data mining, donald trump, elections, language, politics, Post, sentiment analysis, text mining, twitter, Uncategorized, uspoli
On August 6, artist Todd Vaziri observed that all of Trump’s angry tweets come from the Twitter client for Android, while the more presidential, less batshit ones come from an Iphone; Vaziri speculated that the latter were sent by a staffer.
Now, David Robinson’s textual analysis of the two different corpuses show that different people are behind each client’s tweets — they …