  Malicious actors exploit social media to inflate stock prices, sway
elections, spread misinformation, and sow discord. To these ends, they employ
tactics that include the use of inauthentic accounts and campaigns. Methods to
detect these abuses currently rely on features specifically designed to target
suspicious behaviors. However, the effectiveness of these methods decays as
malicious behaviors evolve. To address this challenge, we propose a general
language for modeling social media account behavior. Words in this language,
called BLOC, consist of symbols drawn from distinct alphabets representing user
actions and content. The language is highly flexible and can be applied to
model a broad spectrum of legitimate and suspicious online behaviors without
extensive fine-tuning. Using BLOC to represent the behaviors of Twitter
accounts, we achieve performance comparable to or better than state-of-the-art
methods in the detection of social bots and coordinated inauthentic behavior.
