Twitter

40 posts
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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Trending #TrumpWon didn’t start in Russia

After the first presidential debate, #TrumpWon was a trending topic on Twitter, which led many to believe that there were bots involved — maybe from Russia. It didn’t help that a fake map of Saint Petersburg with a bunch of bubbles on it went viral too. The real reasons for the trending hashtag are much more mundane. Tags: election, Twitter

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Trump tweets from Android vs. iPhone, an angry difference

Sometimes I check Donald Trump’s Twitter feed, as many find themselves doing and quickly regretting. There’s definitely a certain style to some of the tweets. But there are also tweets that don’t seem so “sad!” David Robinson was curious if you can see this difference if you look at tweets sent from an iPhone and those sent from an Android phone. There appears that you can. Angrier tweets tend to...

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Game of Thrones discussions for every episode, visualized

I hear there’s some show called “Game of Thrones” that’s kind of popular these days. Twitter visualized how every episode was discussed, counting the character connections, the emojis used, and the changes over time. See how popular each character was, and the emojis used to described each character. In the visualization below, each circle represents a character with its size proportional to how often the character was mentioned in the...

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Bot automatically generates maps from American Community Survey data

The American Community Survey is an ongoing survey run by the United States Census Bureau that collects data about who we are. The map maker bot by Neil Freeman is a Twitter bot that automatically generates county-level maps based on this ACS data. It’s been running for the past month, making one map per hour, so there are already lots of demographic breakdowns to browse. Pretty awesome. The implementation gets...

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