Many large cities in the U.S. have a problem with violent crime, some of which is committed by gang affiliates. Those individuals use social media platforms like Twitter to express messages of loss and aggression, which can grow in volume and disseminate quickly, often serving as credible signals to commit an imminent violent crime. These tweets may be useful to law enforcement and community service workers who seek to mitigate violent crime by halting the criminal activity. Thus, this research explores the feasibility of automatically ﬁnding criminal signaling of gang members on Twitter and examining the relationship between this signaling and daily crime increase per city. Content and dissemination features from this analysis, along with time series and other auxiliary predictors, are used to train supervised algorithms. It was discovered that several indicators point to credible aggression and credible loss in gang-affiliated social media posts, including the number of followers, user mentions, and the frequency and speed of the retweets. It was also found that credible aggression, along with several other predictors such as weather and past crime instances, were positively associated with violent crime in the subsequent period. Our research shows that knowledge of these indicators has theoretical importance for understanding credible social media posts and later interactive engagement. It also has practical significance for communities to use in mitigating violent crime by finding criminal signals in the virtual space before actual crimes are committed in the physical space.