  The impact of online social media on societal events and institutions is
profound; and with the rapid increases in user uptake, we are just starting to
understand its ramifications. Social scientists and practitioners who model
online discourse as a proxy for real-world behavior, often curate large social
media datasets. A lack of available tooling aimed at non-data science experts
frequently leaves this data (and the insights it holds) underutilized. Here, we
propose birdspotter -- a tool to analyze and label Twitter users --, and
birdspotter.ml -- an exploratory visualizer for the computed metrics.
birdspotter provides an end-to-end analysis pipeline, from the processing of
pre-collected Twitter data, to general-purpose labeling of users, and
estimating their social influence, within a few lines of code. The package
features tutorials and detailed documentation. We also illustrate how to train
birdspotter into a fully-fledged bot detector that achieves better than
state-of-the-art performances without making any Twitter API online calls, and
we showcase its usage in an exploratory analysis of a topical COVID-19 dataset.
