Twitter

3 posts
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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Infinite Twitter ad campaign, based on data profiles

As you probably know, Twitter (and all social media) collects data about you and infers your likes, dislikes, wants, dreams, hopes, etc. Sam Lavigne set up a scraper to find out all the user segments, ranging from “buyers of cheese” to “households with people who have recently moved into a new home.” It can get pretty detailed. Lavigne then used this data to automatically generate an infinite ad campaign, on...

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Where people use certain words

Nikhil Sonnad for Quartz mapped the top 100,000 words used in tweets. Search to your heart’s content. The data for these maps are drawn from billions of tweets collected by geographer Diansheng Guo in 2014. Jack Grieve, a forensic linguist at Aston University in the United Kingdom, along with Andrea Nini of the University of Manchester, identified the top 100,000 words used in these tweets and how often they are...

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@HillaryClinton vs. @realDonaldTrump

A comparison of the words unique to the candidates on Twitter. Read More

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