# Charting the Landscape of Online Cryptocurrency Manipulation

arXiv: https://arxiv.org/abs/2001.10289 · HTML: https://mlsanity.com/paper/2001.10289/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jan 2020.

## Built on by (most cited first, 15 of 15)

- [A Survey on Multimodal Disinformation Detection](https://mlsanity.com/paper/2103.12541/) (2021)
- [Simplistic Collection and Labeling Practices Limit the Utility of Benchmark Datasets for Twitter Bot Detection](https://mlsanity.com/paper/2301.07015/) (2023)
- [AI-Generated Faces in the Real World: A Large-Scale Case Study of Twitter Profile Images](https://mlsanity.com/paper/2404.14244/) (2024)
- [Demystifying Misconceptions in Social Bots Research](https://mlsanity.com/paper/2303.17251/) (2023)
- [Sequence-Based Target Coin Prediction for Cryptocurrency Pump-and-Dump](https://mlsanity.com/paper/2204.12929/) (2022)
- [Giving AI Agents Access to Cryptocurrency and Smart Contracts Creates New Vectors of AI Harm](https://mlsanity.com/paper/2507.08249/) (2025)
- [Machine Learning for Blockchain Data Analysis: Progress and Opportunities](https://mlsanity.com/paper/2404.18251/) (2024)
- [Mapping the Italian Telegram Ecosystem: Communities, Toxicity, and Hate Speech](https://mlsanity.com/paper/2504.19594/) (2025)
- [Linear Laws of Markov Chains with an Application for Anomaly Detection in Bitcoin Prices](https://mlsanity.com/paper/2201.09790/) (2022)
- [Application of AI-based Models for Online Fraud Detection and Analysis](https://mlsanity.com/paper/2409.19022/) (2024)
- [Social-Media Activity Forecasting with Exogenous Information Signals](https://mlsanity.com/paper/2109.11024/) (2021)
- [Topic Modeling Based on Two-Step Flow Theory: Application to Tweets about Bitcoin](https://mlsanity.com/paper/2303.02032/) (2023)
- [Adversarial Botometer: Adversarial Analysis for Social Bot Detection](https://mlsanity.com/paper/2405.02016/) (2024)
- [DLT-Corpus: A Large-Scale Text Collection for the Distributed Ledger Technology Domain](https://mlsanity.com/paper/2602.22045/) (2026)
- [PumpSense: Real-Time Detection and Target Extraction of Crypto Pump-and-Dumps on Telegram](https://mlsanity.com/paper/2605.09431/) (2026)

## Abstract (arXiv)

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.