Charting the Landscape of Online Cryptocurrency Manipulation
arXiv:2001.10289 · doi:10.1109/ACCESS.2020.3003370
Abstract
Cryptocurrencies represent one of the most attractive markets for financial speculation. As a consequence, they have attracted unprecedented attention on social media. Besides genuine discussions and legitimate investment initiatives, several deceptive activities have flourished. In this work, we chart the online cryptocurrency landscape across multiple platforms. To reach our goal, we collected a large dataset, composed of more than 50M messages published by almost 7M users on Twitter, Telegram and Discord, over three months. We performed bot detection on Twitter accounts sharing invite links to Telegram and Discord channels, and we discovered that more than 56% of them were bots or suspended accounts. Then, we applied topic modeling techniques to Telegram and Discord messages, unveiling two different deception schemes - "pump-and-dump" and "Ponzi" - and identifying the channels involved in these frauds. Whereas on Discord we found a negligible level of deception, on Telegram we retrieved 296 channels involved in pump-and-dump and 432 involved in Ponzi schemes, accounting for a striking 20% of the total. Moreover, we observed that 93% of the invite links shared by Twitter bots point to Telegram pump-and-dump channels, shedding light on a little-known social bot activity. Charting the landscape of online cryptocurrency manipulation can inform actionable policies to fight such abuse.
References in corpus (12)
- Emergence of scaling in random networks
- The Rise of Social Bots
- BotOrNot: A System to Evaluate Social Bots
- Bots increase exposure to negative and inflammatory content in online social systems
- Disinformation and Social Bot Operations in the Run Up to the 2017 French Presidential Election
- Arming the public with artificial intelligence to counter social bots
- The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race
- Polyglot: Distributed Word Representations for Multilingual NLP
- Social Fingerprinting: detection of spambot groups through DNA-inspired behavioral modeling
- Cashtag piggybacking: uncovering spam and bot activity in stock microblogs on Twitter
- An Experimental Study of Cryptocurrency Market Dynamics
- People are Strange when you're a Stranger: Impact and Influence of Bots on Social Networks
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- A Decade of Social Bot Detection
- Botometer 101: Social bot practicum for computational social scientists
- The Doge of Wall Street: Analysis and Detection of Pump and Dump Cryptocurrency Manipulations
- How does Twitter account moderation work? Dynamics of account creation and suspension on Twitter during major geopolitical events
- Twitter Spam and False Accounts Prevalence, Detection and Characterization: A Survey
- Simplistic Collection and Labeling Practices Limit the Utility of Benchmark Datasets for Twitter Bot Detection
- Application of AI-based Models for Online Fraud Detection and Analysis
- Towards Understanding and Characterizing the Arbitrage Bot Scam In the Wild
- AI-Generated Faces in the Real World: A Large-Scale Case Study of Twitter Profile Images
- Understanding NFT Price Moves through Tweets Keywords Analysis
- Tracing Cryptocurrency Scams: Clustering Replicated Advance-Fee and Phishing Websites
- A Dataset of Coordinated Cryptocurrency-Related Social Media Campaigns
- Down the bot hole: actionable insights from a 1-year analysis of bots activity on Twitter
- Deciphering Crypto Twitter
- Social-Media Activity Forecasting with Exogenous Information Signals
- Investigating the concentration of High Yield Investment Programs in the United Kingdom