AI Learns Gen Z Language to Decode Online Crime Signals

AI and the Language of Gen Z: A Forensic Linguistics Perspective on Crime Prevention in the Digital Age

Author: Wanitcha Sumanat, president of the Southeast Asian Association of Professional Translators and Interpreters (SEAProTI) and SEAProTI- certified Forensic Linguist

2 November 2025, Bangkok – The Australian Federal Police (AFP) has recently developed an Artificial Intelligence (AI) system designed to understand and decode the “language of Generation Z.” This initiative represents a paradigm shift in digital policing, merging technology, linguistics, and criminology. The AI system is trained to interpret slang, emojis, and online symbols used in encrypted chats, gaming environments, and social media, communication spaces that have become central to youth interaction and, increasingly, to cyber-related criminal activity.

This development directly connects to the discipline of Forensic Linguistics, which studies how language functions within legal and criminal contexts. By teaching AI to analyze hidden meanings, coded expressions, and emotional tones, the AFP’s project integrates linguistic intelligence into digital law enforcement, turning online discourse into actionable intelligence for early crime prevention.

Relevance to Forensic Linguistics
Forensic linguistics extends beyond analyzing written or spoken words, it involves understanding context, intent, and discourse patterns. The AFP’s AI initiative mirrors this analytical process by focusing on the pragmatic and sociolinguistic dimensions of communication. Generation Z often employs dynamic and fast-changing language systems, including emojis, memes, and hybrid expressions, which can mask harmful or extremist intentions.

For example, a combination of emojis such as 🔫💀🔥 might not simply represent gaming symbols but could encode messages about violence or group identity. Traditional surveillance systems are ill-equipped to interpret these subtleties. Therefore, the AI’s linguistic model functions similarly to a forensic linguist detecting concealed meaning, inferring intent, and identifying potential threats based on discourse cues.

This approach transforms forensic linguistics from a post-crime analytical tool (examining evidence after an event) into a predictive model that can identify at-risk communication behaviors before crimes occur.

Linguistic Interpretation and AI Learning
The AFP’s AI system employs machine learning and natural language processing (NLP) to go beyond literal translation and capture contextual meaning. It learns from large datasets of online communication to identify tone, sentiment, and emerging patterns in youth language.

This aligns with Coulthard and Johnson’s (2017) assertion that forensic linguistics is fundamentally about context and intention rather than simple word matching. By incorporating contextual semantics, sociolinguistic variation, and pragmatic inference, AI can better recognize when ordinary digital expressions signal distress, radicalization, or grooming.

In essence, the AFP’s model demonstrates how AI-assisted forensic linguistics can evolve from text analysis into an interpretive science — one that understands language as a living social system rather than a static code.

Social Benefits and Ethical Considerations
The societal impact of this project is profound. By decoding the online communication of at-risk individuals, law enforcement can intervene earlier in cases involving cyberbullying, sexual exploitation, or extremist recruitment. This approach embodies the principle of preventive justice, using linguistic signals to safeguard potential victims — particularly children and teenagers.

Moreover, the system’s ability to detect early signs of manipulation or self-harm may contribute to public safety and mental health awareness. As the AFP Commissioner remarked, “If it once took a village to raise a child, it now takes a nation to protect them.”

Nevertheless, such initiatives raise critical ethical and privacy concerns. Constant linguistic surveillance risks infringing on digital rights and freedom of expression. Thus, the use of forensic linguistics in AI must be guided by strict ethical frameworks ensuring proportionality, accountability, and human oversight.

Conclusion: The Convergence of AI and Forensic Linguistics
The AFP’s project exemplifies the emergence of AI-driven forensic linguistics — a fusion of computational power and linguistic expertise. It demonstrates that understanding the language of youth is not merely a sociocultural exercise but a security imperative in the digital era.

Ultimately, the goal is not surveillance for its own sake but understanding — building safer online communities through linguistic empathy and informed prevention. By decoding the evolving symbols of Gen Z, law enforcement gains insight into the digital psyche of the new generation, turning communication itself into a tool for protection rather than harm.

References (APA 7th Edition)

• Coulthard, M., & Johnson, A. (2017). An Introduction to Forensic Linguistics: Language in Evidence (2nd ed.). Routledge.
• Grant, T. (2022). Quantitative Methods in Forensic Linguistics: An Introduction. Routledge.
• Holt, E., & Johnson, A. (2023). Forensic linguistics and artificial intelligence: Emerging tools in law enforcement. Journal of Language and Law, 10(2), 145–162.
• Australian Federal Police (AFP). (2025, October). AI system for decoding Gen Z language in cybercrime prevention. Canberra: AFP Press Office.

About Certified Translators, Translation Certifiers, and Certified Interpreters of SEAProTI
The Southeast Asian Association of Professional Translators and Interpreters (SEAProTI) has formally announced the qualifications and requirements for registration of Certified Translators, Translation Certification Providers, and Certified Interpreters in Sections 9 and 10 of the Royal Gazette, published by the Secretariat of the Cabinet, Office of the Prime Minister of Thailand, on 25 July 2024 (Vol. 141, Part 66 Ng, p. 100).  Certified Translators, Translation Certification Providers, and Certified Interpreters
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วิเคราะห์เชิงภาษาศาสตร์นิติวิทยาศาสตร์ต่อโครงการ AI ของตำรวจออสเตรเลีย

ผู้แต่ง วณิชชา สุมานัส นายกสมาคมวิชาชีพนักแปลและล่ามแห่งเอเชียตะวันออกเฉียงใต้ และผู้เชี่ยวชาญด้านนิติภาษาศาสตร์

• การวิเคราะห์ความหมายของคำหรืออิโมจิในบริบทของเกมหรือโซเชียล
• การแยกแยะน้ำเสียง (tone) และอารมณ์ (sentiment) ในการสื่อสาร
• การระบุรูปแบบของการสื่อสารที่อาจชี้นำสู่การล่วงละเมิดหรือความรุนแรง

• Coulthard, M., & Johnson, A. (2017). An Introduction to Forensic Linguistics: Language in Evidence (2nd ed.). Routledge.
• Grant, T. (2022). Quantitative Methods in Forensic Linguistics: An Introduction. Routledge.
• Holt, E., & Johnson, A. (2023). “Forensic Linguistics and Artificial Intelligence: Emerging Tools in Law Enforcement.” Journal of Language and Law, 10(2), 145–162.
• Australian Federal Police (AFP). (2025, October). AI System for Decoding Gen Z Language in Cybercrime Prevention. Canberra: AFP Press Office.