Trust Defender is a collection of open source scripts used to train and run a classifier to classify Twitter users as potential good or bad actors. The classifier models are based on Twitter user information and focus heavily on the user bio description to make determinations. https://github.com/texifter/trust-defender There are two primary classifier types: an n-gram naive Bayes classifier model, which uses 2,3,4, and 5-gram models combined and results a probability score for two classes: bot or good, a neural network that takes as input: p(bot) (from the n-gram classifier) p(good) (from the n-gram classifier), number of days the Twitter account has been active, average number of statuses per day, average number of followers per day, average number of per day, the description length in number of individual terms, the number of “lists” in the description (e.g. god, country, president would be considered a “list”), the number of hashtags used in the description, the number of URLs found in the description. Not every seemingly odd account is a "bad actor" or a bot, however, you can find some pretty freaky stuff using this model. Happy hunting. Elon is waiting for your findings. -- Dr. Stuart W. Shulman Founder and CEO, Texifter Editor Emeritus, *Journal of Information Technology & Politics*