  Detecting social bots has evolved into a pivotal yet intricate task, aimed at
combating the dissemination of misinformation and preserving the authenticity
of online interactions. While earlier graph-based approaches, which leverage
topological structure of social networks, yielded notable outcomes, they
overlooked the inherent dynamicity of social networks -- In reality, they
largely depicted the social network as a static graph and solely relied on its
most recent state. Due to the absence of dynamicity modeling, such approaches
are vulnerable to evasion, particularly when advanced social bots interact with
other users to camouflage identities and escape detection. To tackle these
challenges, we propose BotDGT, a novel framework that not only considers the
topological structure, but also effectively incorporates dynamic nature of
social network. Specifically, we characterize a social network as a dynamic
graph. A structural module is employed to acquire topological information from
each historical snapshot. Additionally, a temporal module is proposed to
integrate historical context and model the evolving behavior patterns exhibited
by social bots and legitimate users. Experimental results demonstrate the
superiority of BotDGT against the leading methods that neglected the dynamic
nature of social networks in terms of accuracy, recall, and F1-score.
