Understanding the Cryptocurrency Free Giveaway Scam Disseminated on Twitter Lists
Kai Li, Darren Lee, Shixuan Guan

TL;DR
This paper introduces GiveawayScamHunter, an automated system that detects and analyzes cryptocurrency giveaway scams on Twitter, revealing their scale, tactics, and financial impact with over 95,000 scam lists identified and nearly $872,000 lost by victims.
Contribution
The paper presents a novel automated detection system for Twitter-based cryptocurrency scams, combining NLP and large-scale data collection to analyze scam tactics and impact.
Findings
Detected 95,111 scam lists over a year period.
Identified 327 scam domains and 121 scam addresses.
Estimated financial loss of approximately $872,000.
Abstract
This paper presents a comprehensive analysis of the cryptocurrency free giveaway scam disseminated in a new distribution channel, Twitter lists. To collect and detect the scam in this channel, unlike existing scam detection systems that rely on manual effort, this paper develops a fully automated scam detection system, \textit{GiveawayScamHunter}, to continuously collect lists from Twitter and utilize a Nature-Language-Processing (NLP) model to automatically detect the free giveaway scam and extract the scam cryptocurrency address. By running \textit{GiveawayScamHunter} from June 2022 to June 2023, we detected 95,111 free giveaway scam lists on Twitter that were created by thousands of Twitter accounts. Through analyzing the list creator accounts, our work reveals that scammers have combined different strategies to spread the scam, including compromising popular accounts and creating…
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Taxonomy
TopicsSpam and Phishing Detection · Cybercrime and Law Enforcement Studies · Advanced Malware Detection Techniques
