Online social networks attract billions of active users and deeply influence people’s lifestyles. However

as public social networks with low requirements for registration and joining

it is inevitable that social bots are able to easily register and do harmful things such as controlling public opinion and spreading inaccurate information for profit. Nevertheless

single-site social bot detection systems often rely on historical behavioral data to identify bots

and the detection occurred after the social bots have implemented their attacks. To identify social bots as early as possible

this paper proposes Cerberus

a cross-site system for detecting social bots in social networks

which solves the cold-start problem of user identification caused by insufficient user data on a single platform at an early stage and thus identifies social bots as early as possible. In this paper

the system is designed to identify whether a user on Twitter is a bot or not by leveraging the user’s profile and text contents on his or her Medium account linked to Twitter. The results from our experiments show that the AUC score of the system can reach 0.7522

which outperforms others.