Impromptu Cybercrime Euphemism Detection

TL;DR

This paper introduces a novel dataset and detection framework for impromptu cybercrime euphemisms on social media, employing context augmentation and iterative training, significantly outperforming previous methods.

Contribution

It pioneers impromptu euphemism detection by creating the ICED dataset and proposing a specialized detection framework with multi-round training and context modeling.

Findings

Achieved 76-fold improvement over previous methods.

Developed a new dataset for impromptu euphemism detection.

Demonstrated effectiveness of context augmentation and iterative training.

Abstract

Detecting euphemisms is essential for content security on various social media platforms, but existing methods designed for detecting euphemisms are ineffective in impromptu euphemisms. In this work, we make a first attempt to an exploration of impromptu euphemism detection and introduce the Impromptu Cybercrime Euphemisms Detection (ICED) dataset. Moreover, we propose a detection framework tailored to this problem, which employs context augmentation modeling and multi-round iterative training. Our detection framework mainly consists of a coarse-grained and a fine-grained classification model. The coarse-grained classification model removes most of the harmless content in the corpus to be detected. The fine-grained model, impromptu euphemisms detector, integrates context augmentation and multi-round iterations training to better predicts the actual meaning of a masked token. In addition, we leverage ChatGPT to evaluate the mode's capability. Experimental results demonstrate that our approach achieves a remarkable 76-fold improvement compared to the previous state-of-the-art euphemism detector.