Advances in NLP Techniques for Detection of Message-Based Threats in Digital Platforms: A Systematic Review

Abstract

Users of all ages face risks on social media and messaging platforms. When encountering suspicious messages, legitimate concerns arise about a sender’s malicious intent. This study examines recent advances in Natural Language Processing for detecting message-based threats in digital communication. We conducted a systematic review following PRISMA guidelines, to address four research questions. After applying a rigorous search and screening pipeline, 30 publications were selected for analysis. Our work assessed the NLP techniques and evaluation methods employed in recent threat detection research, revealing that large language models appear in only 20% of the reviewed works. We further categorized detection input scopes and discussed ethical and privacy implications. The results show that AI ethical aspects are not systematically addressed in the reviewed scientific literature.