Machine learning for short message conversational analysis in Law Enforcement

This project aims to identify novel methods for inferring actors, activities, and other elements from short message communications. Covert communications are a specialist domain for analysis in the Law Enforcement (LE) context. In this project we aim to improve law enforcement’s understanding of online criminal communications, exploring texts for automated understanding of intent, sentiment, criminal capability, and involvement.

The Faculty of Information Technology has a mission to advance social good through its research. Key to this mission is the AiLECS (Artificial Intelligence for Law Enforcement and Community Safety) research lab. The AiLECS lab is a joint initiative of Monash University and the Australian Federal Police, and researches the ethical application of AI theories and techniques to problems of interest to law enforcement agencies. The work of the lab is applied in nature, we seek to rapidly translate our research into real-world solutions to significant threats to community safety.

This is a challenging problem as it spans a wide range of topic and contextual domains. Moreover, such language processing must deal with slang, foreign words and passages, localised dialects, unpredictably constructed language, and linguistic obfuscation. All of these will be different depending on the conversational context, and these elements will gradually shift over time. Data for this analysis may come from short text message conversations, and audio  transcriptions.