Fake Twitter Account Distributors – The Pattern


From model bots to Russian bots with English names, there are distinctive patterns that provide substantive, visual evidence that fake accounts are exactly that—fake. But fake account patterns aren’t the only patterns. Some patterns indicate who is responsible for distributing fake account networks across Twitter.

Fake Twitter Account Distributors

Fake Twitter account distributors—and their patterns—are decidedly different from people who simply purchase fake followers. People who purchase fake followers mostly do not follow the fake accounts. Rather, they use the fakes for the sole purpose of falsely inflating their follower numbers.

Fake account distributors, on the other hand, tend to follow the fake accounts. This makes sense when you consider what the fake account distributors need to accomplish. Among other things, fake account distributors need to make the fakes look as legitimate—as real—as possible. Making the accounts look legitimate would naturally include giving legitimate-looking followers to the fakes.

The Fake Account Distributor Pattern

Let’s start with an easily identifiable fake account group.

Note that this fake account group includes:

> Profile pics of young women
> English names and handles that don’t match the name
> Short phrases as profile descriptions
> Locations are in the USA
> Creation date of March 2020
> Similar tweets that are pornographic in nature
> Use of location-related hashtags (unrelated to the tweet subject)

These fakes don’t only share a basic account pattern, however. They also share an in-common follower pattern. In this case, the in-common followers are Russian.

In-Common Follower Pattern

The same three Russian accounts are interspersed across the few followers in all of these accounts.

An entire bot group with the same three followers interspersed across the group provides some evidence that this is planned; it’s purposeful. But there is more to this pattern.

How Did the In-Common Followers Find This Fake Account Group?

Looking at the fake account group’s following, the three, in-common followers are nowhere to be found. (Though, you may notice that they have one in-common account that they each are following; it’s also Russian.)

These are just a few of the accounts with smaller following numbers. However, the pattern holds. Not one account from this fake account group follows even one of the three, in-common followers; and the entire fake account group follows the Rainru account.

The near impossibility of the three, in-common Russian accounts following so many individual, American accounts from the same fake account group (particularly without the accounts following them in return) strongly indicates that the in-common accounts had foreknowledge of the fake account group.

Why are the Patterns Important?

To avoid false flags, it’s important to distinguish between those who follow fake accounts accidentally (the platform is overflowing with fakes, after all) and those who purposefully participate in fake account distribution networks. The distinctive, visual patterns shown above are the solution to this notorious dilemma.

Fake Account Distributors Are Becoming More Sophisticated

While it is true that bots and other types of fake accounts have become more sophisticated over the years, so to have the fake Twitter account distributors. For example, when I began studying the fake account patterns about two years ago, bot distributors were, essentially, lazy. They followed fake account groups in almost exact, sequential order. One after another after another. For instance:

Over time, this sequential pattern has become less common. Now, fake account distributors often break-up the sequence, making it harder to identify the pattern.

For example, let’s look at one of the Russian distributors with which the article began. As you can see, he doesn’t follow the fake account group in exact, sequential order. Instead, other accounts are mixed in-between (many of which are other types of fake accounts).

Whether they are lazy or sophisticated, the people who participate in fake Twitter account distribution networks can’t hide the visual patterns that emerge with such activity. Regretfully, basic algorithms and plot charts are not equipped currently to handle the visual patterns inherent with the fake account networks and their distributors. Instead, tracking down these patterns requires a roll-up-the-sleeve, manual approach—an approach that Twitter itself appears not to want to try.

Written by Virginia Murr

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Author: Virginia Murr