Twitter

40 posts
How a meme grew into a campaign slogan

A meme that cried “jobs not mobs” began modestly, but a couple of weeks later it found its way into a slogan used by the President of the United States. Keith Collins and Kevin Roose for The New York Times traced the spread of the meme through social media using a beeswarm chart. Blue represents activity on Twitter, yellow represents Facebook, and orange represents Reddit. Circles are sized by retweets,...

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Visualizing the toxicity in Twitter conversations

Peter Beshai was tasked with visualizing the toxicity in Twitter conversations. He arrived at this organic-looking model using 3-D visual effects software. Nice. Tags: toxic, Twitter

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Download 3 million Russian troll tweets

Oliver Roeder for FiveThirtyEight: FiveThirtyEight has obtained nearly 3 million tweets from accounts associated with the Internet Research Agency. To our knowledge, it’s the fullest empirical record to date of Russian trolls’ actions on social media, showing a relentless and systematic onslaught. In concert with the researchers who first pulled the tweets, FiveThirtyEight is uploading them to GitHub so that others can explore the data for themselves. The data set...

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Twitter bot purge

With Twitter cracking down, some users are experiencing bigger dips in follower count than others. Jeremy Ashkenas charted some of the drops. Tags: fake, Twitter

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Changing Twitter, with Statistics

Earlier this year, The New York Times investigated fake followers on Twitter showing very clearly that it was a problem. It’s hard to believe that Twitter didn’t already know about the scale of the issue, but after the story, the social service finally started to work on the problem. Nicholas Confessore and Gabriel J.X. Dance for The New York Times: An investigation by The New York Times in January demonstrated...

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Tweeting a map of every Census tract in the United States

By Neil Freeman, the @everytract bot on Twitter, as the name suggests, is tweeting a map of every Census tract in numerical order. It’s one map each half hour. Census data, or data in general really, is typically in aggregate or about the overall trends, which requires an abstract view of a bunch of data points pushed together. So it’s nice to see a straightforward project put focus on the...

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Bot or Not: A Twitter user classifier

Michael W. Kearney implemented a classifier for Twitter bots. It’s called botornot: Uses machine learning to classify Twitter accounts as bots or not bots. The default model is 93.53% accurate when classifying bots and 95.32% accurate when classifying non-bots. The fast model is 91.78% accurate when classifying bots and 92.61% accurate when classifying non-bots. Overall, the default model is correct 93.8% of the time. Overall, the fast model is correct...

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Tracking Historical Twitter Followers: @elisewho vs. @stiles

My wife (@elisewho) and I (@stiles) had a silly social media moment yesterday when I replied to one of her tweets — despite the fact that she was sitting in an adjacent room of our Seoul apartment. USC professor Robert Hernandez (a.k.a. @webjournalist) captured it:   Among my favorite media couples are @elisewho and @stiles. pic.twitter.com/HLp3g90Tgc — Robert is in S. Korea (@webjournalist) February 12, 2018 The exchange, which we both...

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Finding fake followers

This fake follower piece by Nicholas Confessore, Gabriel J.X. Dance, Richard Harris, and Mark Hansen for The New York Times is tops. In search of shortcuts to greater influence, many buy followers, likes, and retweets on Twitter. The numbers go up, but a lot of extra “influence” is just automated fluff. The Times focuses on one company, Devumi, and investigates the follower pattern of some of the customers, as shown...

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LaVar Ball trolling to the top

I didn’t know who LaVar Ball was, and suddenly, it was non-stop sports news about the Ball family. If you’re unfamiliar, LaVar Ball is the father of a now professional basketball player. Before his son was drafted by the Los Angeles Lakers, Ball garnered attention for saying trollish things like he could’ve beat Michael Jordan one-on-one in his heyday. Anthony Olivieri for ESPN outlines the rise of the loud talker...

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