  Arabic Twitter space is crawling with bots that fuel political feuds, spread
misinformation, and proliferate sectarian rhetoric. While efforts have long
existed to analyze and detect English bots, Arabic bot detection and
characterization remains largely understudied. In this work, we contribute new
insights into the role of bots in spreading religious hatred on Arabic Twitter
and introduce a novel regression model that can accurately identify Arabic
language bots. Our assessment shows that existing tools that are highly
accurate in detecting English bots don't perform as well on Arabic bots. We
identify the possible reasons for this poor performance, perform a thorough
analysis of linguistic, content, behavioral and network features, and report on
the most informative features that distinguish Arabic bots from humans as well
as the differences between Arabic and English bots. Our results mark an
important step toward understanding the behavior of malicious bots on Arabic
Twitter and pave the way for a more effective Arabic bot detection tools.
