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Twitter bot detection is vital in combating misinformation and safeguardingthe integrity of social media discourse. While malicious bots are becoming moreand more sophisticated and personalized, standard bot detection approaches arestill agnostic to social environments (henceforth, communities) the botsoperate 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 andfeature-, text-, and graph-based models, adjusted to a particular community onTwitter. We introduce an approach that performs confidence calibration acrossbot detection models, which addresses generalization issues in existingcommunity-agnostic models targeting individual bots and leads to more accuratecommunity-level bot estimations. Experiments demonstrate that BotPercentachieves state-of-the-art performance in community-level Twitter bot detectionacross both balanced and imbalanced class distribution settings, %outperformingexisting approaches and presenting a less biased estimator of Twitter botpopulations within the communities we analyze. We then analyze bot rates inseveral Twitter groups, including users who engage with partisan news media,political communities in different countries, and more. Our results reveal thatthe presence of Twitter bots is not homogeneous, but exhibiting aspatial-temporal distribution with considerable heterogeneity that should betaken into account for content moderation and social media policy making. Theimplementation of BotPercent is available athttps://github.com/TamSiuhin/BotPercent.

BotPercent: Estimating Bot Populations in Twitter Communities