Along with the public web-based version linked to above, there is a GitHub repository with the code so that the entire front-end can be self-hosted.
Someone has set up a bot on Twitter that does nothing but tweet out Wikipedia titles that could be sung to the Teenage Mutant Ninja Turtles theme song.
This took them a ridiculously long time to implement, but I guess better late than never is still a thing.
Havard’s Dataverse project has, among other things, a downloadable dataset of “all known publicy available tweets for Donald J. Trump’s (@realdonaldtrump) Twitter account” as a JSON file.
This data was compiled from multiple sources including several online Github accounts that contained the status ids for previous tweets made by Donald Trump. All ids were compiled into a single list and then those ids were requested from Twitter’s “statuses lookup” endpoint. Tweets deleted by Donald Trump will not be in this dataset but can be obtained from the author of this publication for a subset of the time range present in this dataset. This dataset will also include the tweet information for any retweeted tweets under the “retweeted_status” key for each JSON object. The user object has been left in each tweet (both the main tweet and retweeted / quoted tweets if they exist).
ESPN’s Seth Wickersham has an excellent look at the inside workings of the worst team in football and, apparently, one of the most poorly run organizations in sports–the Cleveland Browns.
The version of the Browns that emerges from Wickersham’s profile is the mother of all train wrecks, and this is typical of the sort of “can’t quite get anything right” nature of the organization.
Marketing executives wanted employees to see how fans were engaging with the Browns on social media, so they projected the Browns feed onto a giant wall at the facility. It was like broadcasting talk radio over the entire building, and one day in particular, it was worse than that. One of the marketing staffers entered a search for #dp — for Dawg Pound. The problem was, that hashtag carried a few different meanings, one of which triggered an array of porn to be broadcast onto a wall for the entire office to see for more than 20 minutes, until a tech employee killed the feed.