The classification is based on an R package for Generalized Boosted Regression Models[1]. Can anyone knowledgeable opine about this choice?
What I'd want to also see however, would be curation of a relevant corpus of tweet content that could be integrated in the classification process in terms of actual tweet content (bag of words, sentence structure, & other NLP features, images, other media) being used to determine a given account's similarity between bots and non-bots -- instead of relying essentially on metadata similarity in addition to crude auto-extracted text features like simple total number of words and character counts. I can't say for sure if it'd improve predictions but would seem key for this type of problem.