  Malicious actors create inauthentic social media accounts controlled in part
by algorithms, known as social bots, to disseminate misinformation and agitate
online discussion. While researchers have developed sophisticated methods to
detect abuse, novel bots with diverse behaviors evade detection. We show that
different types of bots are characterized by different behavioral features. As
a result, supervised learning techniques suffer severe performance
deterioration when attempting to detect behaviors not observed in the training
data. Moreover, tuning these models to recognize novel bots requires retraining
with a significant amount of new annotations, which are expensive to obtain. To
address these issues, we propose a new supervised learning method that trains
classifiers specialized for each class of bots and combines their decisions
through the maximum rule. The ensemble of specialized classifiers (ESC) can
better generalize, leading to an average improvement of 56\% in F1 score for
unseen accounts across datasets. Furthermore, novel bot behaviors are learned
with fewer labeled examples during retraining. We deployed ESC in the newest
version of Botometer, a popular tool to detect social bots in the wild, with a
cross-validation AUC of 0.99.
