Detecting and Understanding the Promotion of Illicit Goods and Services on Twitter

In this study, we reveal, for the first time, popular online social networks (especially Twitter) are being extensively abused by miscreants to promote illicit goods and services of diverse categories. This study is made possible by multiple machine learning tools that are designed to detect and analyze Posts of Illicit Promotion (PIPs) as well as revealing their underlying promotion campaigns. Particularly, we observe that PIPs are prevalent on Twitter, along with extensive visibility on other three popular OSNs including YouTube, Facebook, and TikTok. For instance, applying our PIP hunter to the Twitter platform for 6 months has led to the discovery of 12 million distinct PIPs which are widely distributed in 5 major natural languages and 10 illicit categories, e.g., drugs, data leakage, gambling, and weapon sales. Along the discovery of PIPs are 580K Twitter accounts publishing PIPs as well as 37K distinct instant messaging accounts that are embedded in PIPs and serve as next hops of communication with prospective customers. Also, an arms race between Twitter and illicit promotion operators is also observed. Especially, 90% PIPs can survice the first two months since getting published on Twitter, which is likely due to the diverse evasion tactics adopted by miscreants to masquerade PIPs.

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Publication Details

Published
2025-04-22
DOI
https://doi.org/10.1145/3696410.3714550
Citations
1
Primary Topic
Cybercrime and Law Enforcement Studies
Type
article
Field-Weighted Citation Impact
3.25
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article

Detecting and Understanding the Promotion of Illicit Goods and Services on Twitter

Rong‐Bin Huang, Xianghang Mi, Ying Li, Hongyu Wang
1 citations
Cybercrime and Law Enforcement Studies
3.25
article

Detecting and Understanding the Promotion of Illicit Goods and Services on Twitter

Rong‐Bin Huang, Xianghang Mi, Ying Li, Hongyu Wang
article en
1 citations

Abstract

In this study, we reveal, for the first time, popular online social networks (especially Twitter) are being extensively abused by miscreants to promote illicit goods and services of diverse categories. This study is made possible by multiple machine learning tools that are designed to detect and analyze Posts of Illicit Promotion (PIPs) as well as revealing their underlying promotion campaigns. Particularly, we observe that PIPs are prevalent on Twitter, along with extensive visibility on other three popular OSNs including YouTube, Facebook, and TikTok. For instance, applying our PIP hunter to the Twitter platform for 6 months has led to the discovery of 12 million distinct PIPs which are widely distributed in 5 major natural languages and 10 illicit categories, e.g., drugs, data leakage, gambling, and weapon sales. Along the discovery of PIPs are 580K Twitter accounts publishing PIPs as well as 37K distinct instant messaging accounts that are embedded in PIPs and serve as next hops of communication with prospective customers. Also, an arms race between Twitter and illicit promotion operators is also observed. Especially, 90% PIPs can survice the first two months since getting published on Twitter, which is likely due to the diverse evasion tactics adopted by miscreants to masquerade PIPs.

University of Science and Technology of China (CN), University of California, Los Angeles (US)
Reduced inequalities
Openalex Percentile: Top 10%
Cybercrime and Law Enforcement Studies
3.25
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Detecting and Understanding the Promotion of Illicit Goods and Services on Twitter — Rong‐Bin Huang, Xianghang Mi, et al. · (2025) | TGRS Research Map | TGRS