Why It’s So Hard to Count Twitter Bots


It’s Twitter account @ElonMusk a bot? One of the best algorithms for detecting fake accounts he thinks it might bewhich shows how difficult it is to quantify the proportion of fake accounts on the social network.

Counting Twitter bots has become a hotbed in Elon Musk’s $ 44 billion acquisition of Twitter. Last Friday, the billionaire he tweeted that it was putting its purchase “temporarily on hold” until the company provided details to support its claim (as stated in its latest filing with the SEC) that less than 5 percent of “users Monetizable Daily Assets “on Twitter are spam or fake. Musk is also described a plan to count himself the robots involved in sampling 100 @Twitter followers to see how many bots were and said the approach suggests that more than 20 percent of accounts are fake.

But accurately quantifying the percentage of bots on Twitter is much more difficult, according to experts.

Finding them is not difficult if you know where to look. Some accounts, including Musk’s, seem to attract many. “If you just mention Elon Musk on Twitter, you immediately commit to a bunch of cryptographic robots,” says Chris Bail, a sociology professor at Duke University who studies social media.

Twitter is not the only social network that fights fake accounts. Facebook eliminates billions of fake accounts every year. But it’s hard to know for sure that a Twitter account is a bot, as legitimate users may have few followers, rarely tweet, or have weird usernames. It is even more difficult to measure the number of bots operating across the platform as a whole.

To test Musk’s proposal methodologyIV.ai, an AI company, analyzed 100 accounts tracked by Musk carmaker Tesla on Twitter.

An algorithmic examination of the accounts on Tuesday found that more than 20 accounts out of 100 have a high probability of being robots. A manual examination of the same 100 concluded that more than half may be robots. And an analysis of the issues covered by these accounts found no evidence that any of the suspicious accounts were promotional. But many of those accounts also disappeared soon after, suggesting that Twitter is catching robots pretty quickly. Vince Lynch, CEO of IV.ai, says the identification of questionable accounts is also inherently subjective and involves some degree of uncertainty.

“It’s a very difficult problem,” says Filippo Menczer, a Indiana University professor who led the development of the Botometer algorithm, which gave Musk a relatively high bot score. Menczer says looking at 100 accounts will not be representative of daily active Twitter users, and different samples will produce very different results. “I want to expect this to be a joke,” Menczer says of the methodology.

Automated accounts have become more sophisticated and complex in recent years. Many fake accounts are partly operated by humans as well as machines, or simply amplify messages written by real people (what Menczer calls “cyborg accounts”). Other accounts use tricks designed to evade human and algorithmic detection, such as liking and not liking them quickly, or posting and deleting tweets. And, of course, there are many automated or semi-automated accounts, such as those run by many companies, that aren’t really harmful.





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