  Twitter bot detection is vital in combating misinformation and safeguarding
the integrity of social media discourse. While malicious bots are becoming more
and more sophisticated and personalized, standard bot detection approaches are
still agnostic to social environments (henceforth, communities) the bots
operate at. In this work, we introduce community-specific bot detection,
estimating the percentage of bots given the context of a community. Our method
-- BotPercent -- is an amalgamation of Twitter bot detection datasets and
feature-, text-, and graph-based models, adjusted to a particular community on
Twitter. We introduce an approach that performs confidence calibration across
bot detection models, which addresses generalization issues in existing
community-agnostic models targeting individual bots and leads to more accurate
community-level bot estimations. Experiments demonstrate that BotPercent
achieves state-of-the-art performance in community-level Twitter bot detection
across both balanced and imbalanced class distribution settings, %outperforming
existing approaches and presenting a less biased estimator of Twitter bot
populations within the communities we analyze. We then analyze bot rates in
several Twitter groups, including users who engage with partisan news media,
political communities in different countries, and more. Our results reveal that
the presence of Twitter bots is not homogeneous, but exhibiting a
spatial-temporal distribution with considerable heterogeneity that should be
taken into account for content moderation and social media policy making. The
implementation of BotPercent is available at
https://github.com/TamSiuhin/BotPercent.
