BotPercent: Estimating Bot Populations in Twitter Communities
BotPercent estimates community-specific Twitter bot populations by calibrating detection models across social contexts, revealing heterogeneous spatial-temporal bot distributions and achieving state-of-the-art community-level detection accuracy.
Published 202316 citationsPaper ↗
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BotPercent delivers a calibrated, state-of-the-art framework for estimating heterogeneous community-level bot rates, though critics question whether calibrated ensemble averages resolve bot ontology or yield actionable policy.
Abstract
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 communityspecific 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-ofthe-art performance in community-level Twitter bot detection across both balanced and imbalanced class distribution settings, 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.