This is a visualization of the process behind @nyt_first_said. Each day, a script scrapes new articles from nytimes.com. That text is tokenized, or split into words based on whitespace and punctuation. Each word then must pass several criteria. Containing a number or special character is criteria for disqualification. To avoid proper nouns, all capitalized words are filtered. The most important check is against the New York Time's archive search service. The archive goes back to 1851 and contains more than 13 million articles. The paper publishes many thousands of words each day, but only a very few are firsts.

What Microsoft could have learned from veteran botmakers on Twitter.

It sometimes seems that automated bots are taking over social media and driving human discourse. But some (real) researchers aren’t so sure. --- Defining the bot is a tricky problem; technically, it could be any automated account, like a news aggregator, or amplification software, like Hootsuite. Mr. Kazemi found many bots tweeting about Covid-19, including neighborhood health clinics using marketing software to post daily pandemic P.S.A.s about washing your hands. He also found that humans were often mistaken for bots. Consider the “grandpa effect,” as he called it: people who were mistaken for bots because they used social media in “uncool or gauche” ways, he said. Users fond of hitting the share button on news articles also resulted in false positives. This led Mr. Kazemi to wonder whether Botometer should be renamed “Normiemeter.” He tweeted: “Can you imagine the headlines? ‘50% of accounts tweeting about Covid are normies.’”

Just because something is going to break in the end, doesn't mean that it can't have an effect that lasts into the future. Joy, wonder, laughter, hope—the world can be better because of what you built in the past.