bot

5 posts
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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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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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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Algorithmic search for a girlfriend

Sharif Corinaldi moved from New York to Berkeley for graduate school and was in search of a mate. However, after a bit of non-success with online dating sites, he figured a 0.0025 percent chance of finding a match, which meant about 400 messages sent before any success. So, he built a bot to browse and search for him. He accidentally left it running one night. I fiddled with the model...

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