... but edgy
A group of computer scientists have developed a machine-learning algorithm that can sniff out fake profiles lurking on social networks. It’s likely that you and your Facebook friends have the same mutual friends. And on Twitter, it’s also probable that your followers also follow the same people you do too due to common …
Put it in place
It has been in place for a few years in Facebook for a different reason - countering SPAM campaigns and loss of advertising revenue.
You really need to throw quite a few more variables into the equation such as login frequency, login source, tracking actual message flows, likes, etc to get better targeting. Even then, you are likely to flag false positives for the most obvious case - when a "campaign manager" asks all of the company employees to "promote this link on social media".
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This appears to be a very theoretical exercise on simulated networks. Until they actually try it on a real social network, the results are of very limited values. Also, if you are using simple algorithm it will be a very short time before the trolls find a way around it. ... Anonymous Coward
Hi, Anonymous Coward,
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I deliberately keep my activity on social networks for different things. My Instagram has photos that I've taken, my FB is for keeping track of my friends, my Twitter is unrelenting HPC & geekiness. Consequently I probably have very different followers on each.
Is it just me, so does the time necessary to publish a computer security study quite often invalidate the results? By the time you've got your paper together, and gotten it reviewed, the bad actors have already moved on to a new approach.
Just an observation, but this is why I decided not to pursue a career in computer science, it seems like business is at the forefront of computer technology, not academics.
For nearly 2 decades, Google has been doing similar analysis to identify sites attempting to game their algorithm (link farms and the like). The big players are certainly capable of identifying the bad actors - the question is - are they motivated to do anything about it? For example, if Facebook weeds out a load of fake accounts, suddenly those ad view numbers look a bit less impressive to advertisers. Perhaps grilling the CEOs in front of congress/parliament provides some incentive but I doubt it. When (if) the users start closing their accounts in significant numbers we might see some action on this.
If it were possible to run this sort of analysis on publicly available data (I suspect it may be on Twitter), some enterprising soul could come up with a blocking script, Fake Block Pro if you will. I'd sign up to that.
Running with that idea gives you the ability to block trolls, or even accounts with a certain political proclivity - Snowflake Block Pro / Swivel-eyed Loon Block Pro depending on your preferences.
And our echo-chambers will no longer be troubled with any ripples of dissent. Or would we then find that our social networks consist entirely of bots designed to mirror our worldview?
Ah to hell with it, I'm unplugging the internet.
Then it immediately follows that Yet Another Learning Machine can be wrapped around this 'AI engine' to learn how to out-fake the Fake Detector.
The real question becomes, Why didn't they, the AI boffins, already do that after lunch? (They're thick.)
And yes, the same argument is extensible to endless layers, alternating back and forth.
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