In recent years, researchers have shown a vested interest in developing advanced information technologies, machine-learning algorithms, and risk-assessment tools to detect and analyze radical content online, with increased attention on identifying violent extremists or measuring digital pathways of violent radicalization. Yet overlooked in this evolving space has been a systematic examination of what constitutes radical posting behaviors in general. This study uses a sentiment analysis-based algorithm that adapts criminal career measures – and is guided by communication research on social influence – to develop and describe three radical posting behaviors (high-intensity, high-frequency, and high-duration) found on a sub-forum of the most conspicuous right-wing extremist forum. The results highlight the multi-dimensional nature of radical right-wing posting behaviors, many of which may inform future risk factor frameworks used by law enforcement and intelligence agencies to identify credible threats online.