Towards Dark Jargon Interpretation in Underground Forums

Dominic Seyler, Wei Liu, Xiao Feng Wang, Cheng Xiang Zhai

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Dark jargons are benign-looking words that have hidden, sinister meanings and are used by participants of underground forums for illicit behavior. For example, the dark term “rat” is often used in lieu of “Remote Access Trojan”. In this work we present a novel method towards automatically identifying and interpreting dark jargons. We formalize the problem as a mapping from dark words to “clean” words with no hidden meaning. Our method makes use of interpretable representations of dark and clean words in the form of probability distributions over a shared vocabulary. In our experiments we show our method to be effective in terms of dark jargon identification, as it outperforms another baseline on simulated data. Using manual evaluation, we show that our method is able to detect dark jargons in a real-world underground forum dataset.

Original languageEnglish (US)
Title of host publicationAdvances in Information Retrieval - 43rd European Conference on IR Research, ECIR 2021, Proceedings
EditorsDjoerd Hiemstra, Marie-Francine Moens, Josiane Mothe, Raffaele Perego, Martin Potthast, Fabrizio Sebastiani
Number of pages8
ISBN (Print)9783030722395
StatePublished - 2021
Event43rd European Conference on Information Retrieval, ECIR 2021 - Virtual, Online
Duration: Mar 28 2021Apr 1 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12657 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference43rd European Conference on Information Retrieval, ECIR 2021
CityVirtual, Online


  • Dark jargon
  • Hidden meaning interpretation
  • NLP

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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