Elon Musk’s obsession with Twitter bots misses the point entirely

Based on our knowledge and experience, we believe that estimating the percentage of bots on Twitter has become a very difficult task, and debating the accuracy of the estimate might be missing the point. Here’s why.

What, exactly, is a bot?

To measure the prevalence of problematic accounts on Twitter, a clear definition of the targets is necessary. Common terms such as “fake accounts,” “spam accounts,” and “bots” are used interchangeably, but they have different meanings. Fake or false accounts are those that impersonate people. Accounts that mass-produce unsolicited promotional content are defined as spammers. Bots, on the other hand, are accounts controlled in part by software; they may post content or carry out simple interactions, like retweeting, automatically.

These types of accounts often overlap. For instance, you can create a bot that impersonates a human to post spam automatically. Such an account is simultaneously a bot, a spammer, and a fake. But not every fake account is a bot or a spammer, and vice versa. Coming up with an estimate without a clear definition only yields misleading results.

Defining and distinguishing account types also can inform proper interventions. Fake and spam accounts degrade the online environment and violate platform policy. Malicious bots are used to spread misinformation, inflate popularity, exacerbate conflict through negative and inflammatory content, manipulate opinions, influence elections, conduct financial fraud, and disrupt communication. However, some bots can be harmless—or even useful, for example, by helping disseminate news, delivering disaster alerts, and conducting research.

Simply banning all bots is not in the best interest of social media users.

For simplicity, researchers use the term “inauthentic accounts” to refer to the collection of fake accounts, spammers, and malicious bots. This is also the definition Twitter appears to be using. However, it’s unclear what Musk has in mind.

Hard to count

Even when a consensus is reached on a definition, there are still technical challenges to estimating prevalence.

External researchers do not have access to the same data as Twitter, such as IP addresses and phone numbers. This hinders the public’s ability to identify inauthentic accounts. But even Twitter acknowledges that the actual number of inauthentic accounts could be higher than it has estimated because detection is challenging.

Inauthentic accounts evolve and develop new tactics to evade detection. For example, some fake accounts use AI-generated faces as their profiles. These faces can be indistinguishable from real ones, even to humans. Identifying such accounts is hard and requires new technologies.

Another difficulty is posed by coordinated accounts that appear to be normal individually but act so similarly to each other that they are almost certainly controlled by a single entity. Yet they’re like needles in the haystack of hundreds of millions of daily tweets.

Finally, inauthentic accounts can evade detection by techniques like swapping handles or automatically posting and deleting large volumes of content.

The distinction between inauthentic and genuine accounts gets more and more blurry. Accounts can be hacked, bought, or rented, and some users “donate” their credentials to organizations who post on their behalf. As a result, so-called cyborg accounts are controlled by both algorithms and humans. Similarly, spammers sometimes post legitimate content to obscure their activity.

We have observed a broad spectrum of behaviors mixing the characteristics of bots and people. Estimating the prevalence of inauthentic accounts requires applying a simplistic binary classification: authentic or inauthentic. No matter where the line is drawn, mistakes are inevitable.

Missing the big picture

The focus of the recent debate on estimating the number of Twitter bots oversimplifies the issue and misses the point of quantifying the harm of online abuse and manipulation by inauthentic accounts.