Project SHARD (Signature Hash Aggregation for Risk Detection)—a novel framework for predicting antisocial behavior on social media platforms through the systematic collection and analysis of behavioral trait similarities. Moving beyond conventional hashtag-based monitoring and file-hashing systems, SHARD introduces the concept of Behavioral Trait Vectors (BTVs) : cumulative digital signatures derived from recurrent behavioral patterns "leaked" through routine social media activity. Current approaches to content moderation rely primarily on reactive detection—identifying harmful content after publication—or on hash-sharing databases that block exact-match files . While effective for preventing re-upload of known abusive content, these methods fail to identify emerging threats, coordinated bad actors, or individuals exhibiting behavioral patterns predictive of future antisocial conduct. SHARD addresses this gap by establishing a shared database of behavioral trait similarities. Rather than hashing files, the framework hashes human behavioral signatures—creating composite profiles based on linguistic patterns, interaction dynamics, and usage behaviors that collectively define a "type" of user. This database enables platforms to detect antisocial behavior before it fully manifests, supporting proactive moderation and early intervention. The framework draws upon recent advances in behavioral biometrics, machine learning classification, and antisocial behavior prediction research