  Telegram is one of the most used instant messaging apps worldwide. Some of
its success lies in providing high privacy protection and social network
features like the channels -- virtual rooms in which only the admins can post
and broadcast messages to all its subscribers. However, these same features
contributed to the emergence of borderline activities and, as is common with
Online Social Networks, the heavy presence of fake accounts. Telegram started
to address these issues by introducing the verified and scam marks for the
channels. Unfortunately, the problem is far from being solved. In this work, we
perform a large-scale analysis of Telegram by collecting 35,382 different
channels and over 130,000,000 messages. We study the channels that Telegram
marks as verified or scam, highlighting analogies and differences. Then, we
move to the unmarked channels. Here, we find some of the infamous activities
also present on privacy-preserving services of the Dark Web, such as carding,
sharing of illegal adult and copyright protected content. In addition, we
identify and analyze two other types of channels: the clones and the fakes.
Clones are channels that publish the exact content of another channel to gain
subscribers and promote services. Instead, fakes are channels that attempt to
impersonate celebrities or well-known services. Fakes are hard to identify even
by the most advanced users. To detect the fake channels automatically, we
propose a machine learning model that is able to identify them with an accuracy
of 86%. Lastly, we study Sabmyk, a conspiracy theory that exploited fakes and
clones to spread quickly on the platform reaching over 1,000,000 users.
