Conspiracy to Commit: Information Pollution, Artificial Intelligence, and Real-World Hate Crime

Is demand for conspiracy theories online linked to real-world hate crimes? By analyzing online search trends for 36 racially- and politically-charged conspiracy theories in Michigan (2015–2019), we employ a one-dimensional convolutional neural network (1D-CNN) to predict hate crime occurrences offline. A subset of theories—including the Rothschilds family, Q-Anon, and The Great Replacement—improves prediction accuracy, with effects emerging two to three weeks after fluctuations in searches. However, most theories showed no clear connection to offline hate crimes. Aligning with neutralization and differential association theories, our findings empirically link specific racially-charged conspiracy theories to real-world violence. Just as well, this study underscores the potential for machine learning to be used in identifying harmful online patterns and advancing social science research.