Detecting and Understanding the Promotion of Illicit Goods and Services on Twitter
Hongyu Wang, Ying Li, Ronghong Huang, Xianghang Mi
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. CCS Concepts • Security and privacy → Social network security and privacy.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Cited by top-tier papers1
Ask how each one uses itBuilds on12
- Investigating Commercial Pay-Per-Install and the Distribution of Unwanted SoftwareKurt Thomas, Juan A. Elices Crespo, Ryan Rasti, Jean-Michel Picod et al.USENIX Security 2016 · 77 citations
- Measuring PUP Prevalence and PUP Distribution through Pay-Per-Install ServicesPlaton Kotzias, Leyla Bilge, Juan CaballeroUSENIX Security 2016 · 74 citations
- Reading Thieves' Cant: Automatically Identifying and Understanding Dark Jargons from Cybercrime MarketplacesKan Yuan, Haoran Lu, Xiaojing Liao, XiaoFeng WangUSENIX Security 2018 · 56 citations
- Self-Supervised Euphemism Detection and Identification for Content ModerationWanzheng Zhu, Hongyu Gong, Rohan Bansal, Zachary Weinberg et al.S&P 2021 · 56 citations
- Stealthy Porn: Understanding Real-World Adversarial Images for Illicit Online PromotionKan Yuan, Di Tang, Xiaojing Liao, XiaoFeng Wang et al.S&P 2019 · 49 citations
Related papers
- Understanding Cross-Platform Referral Traffic for Illicit Drug PromotionMingming Zha, Zilong Lin, Siyuan Tang, Xiaojing Liao et al.CCS 2024
- Clues in Tweets: Twitter-Guided Discovery and Analysis of SMS SpamSiyuan Tang, Xianghang Mi, Ying Li, XiaoFeng Wang et al.CCS 2022 · 31 citations
- Behind the Tube: Exploitative Monetization of Content on YouTubeAndrew Chu, Arjun Arunasalam, Muslum Ozgur Ozmen, Z. Berkay CelikUSENIX Security 2022
- #Twiti: Social Listening for Threat IntelligenceHyejin Shin, WooChul Shim, Saebom Kim, Sol Lee et al.WWW 2021 · 32 citations
- The Pod People: Understanding Manipulation of Social Media Popularity via Reciprocity AbuseJanith Weerasinghe, Bailey Flanigan, Aviel J. Stein, Damon McCoy et al.WWW 2020 · 24 citations
