  Interest surrounding cryptocurrencies, digital or virtual currencies that are
used as a medium for financial transactions, has grown tremendously in recent
years. The anonymity surrounding these currencies makes investors particularly
susceptible to fraud---such as "pump and dump" scams---where the goal is to
artificially inflate the perceived worth of a currency, luring victims into
investing before the fraudsters can sell their holdings. Because of the speed
and relative anonymity offered by social platforms such as Twitter and
Telegram, social media has become a preferred platform for scammers who wish to
spread false hype about the cryptocurrency they are trying to pump. In this
work we propose and evaluate a computational approach that can automatically
identify pump and dump scams as they unfold by combining information across
social media platforms. We also develop a multi-modal approach for predicting
whether a particular pump attempt will succeed or not. Finally, we analyze the
prevalence of bots in cryptocurrency related tweets, and observe a significant
increase in bot activity during the pump attempts.
