Fake “like” factories – how we reverse engineered facebooks user IDs [video]

Re:Fake “like” factories – how we reverse engineered faceboo... by peterwwillis on Monday December 30, 2019 @10:43AM (#21914037)

I think there's already an answer to this. In a large organization, such a pervasive and difficult problem can't be solved in a direct fashion, because there's always some weird alternate consideration that keeps it from getting tackled. Only a top-down initiative from C-level implemented across the organization has a chance at rooting it out, and if such an initiative existed we'd have heard about it and gotten yearly progress reports.

I think what we're seeing instead is the same as counterfeits or promoted search results at Amazon: solving it could become an existential threat to either popularity or profit, so they're just "managing" it as BAU.

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Re:Fake “like” factories – how we reverse engineered faceboo... by ethbro on Monday December 30, 2019 @05:19PM (#21917171)

I guess one alternative explanation would be that it's a hard problem, and Facebook doesn't know how to solve it. Or that current efforts aren't having good results.

In either case, Facebook has a financial incentive to say nothing, instead of publicizing lackluster results.

Or, my guess, they'd rather keep any news about the existence of this off the front pages, as broader knowledge of its existence by their (not as technically informed) advertising customers only negatively impacts Facebook.

Kind of how a beachfront town wouldn't want to advertise the fact that Great White sharks exist... at all.

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Re:Fake “like” factories – how we reverse engineered faceboo... by mirekrusin on Monday December 30, 2019 @05:44PM (#21917354)

I don’t buy it, we’re building ai that can drive, classify scans better than professional doctors, translate - but we can’t figure out fake accounts? Come on.

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Re:Fake “like” factories – how we reverse engineered faceboo... by braythwayt on Monday December 30, 2019 @08:45PM (#21918332)

Driving, diagnosing, and translating are not good comparisons, because none of those are adversarial.

The problem here is that adversaries are adjusting to whatever measures are put in place. In that respect it might be more like winning at Chess or Go. Which computers can do, but it’s decidedly non-trivial.

And here’s the kicker: If we postulate some hand-wavium big-data/machine-learning/AI that can detect bots and adjust when the bots evolve, why can’t we also postulate some hand-wavium big-data/machine-learning/AI that can run bots and evade the bot detection?

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Re:Fake “like” factories – how we reverse engineered faceboo... by nradov on Tuesday December 31, 2019 @12:11AM (#21919114)

Driving often is adversarial, and no AI can actually drive yet (outside of some very limited circumstances on a small number of roads).

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Re:Fake “like” factories – how we reverse engineered faceboo... by mr__y on Monday December 30, 2019 @07:06PM (#21917841)

It's probably not that they can't classify fake accounts but that doing so at a scale requires too large resources and it's more economical to employ only simpler techniques that weed out most of the bots and live with the remaining few.

Then if they also happen to anyhow benefit from the existence of those fake accounts then it becomes even harder to justify costly filters and leads to a sufficient-enough-to-not-look-obviously-weak grade filtering

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Re:Fake “like” factories – how we reverse engineered faceboo... by rhizome on Monday December 30, 2019 @06:37PM (#21917678)

If it's so problematic, they shouldn't have released the feature(s) in the first place. They unleashed a firehose (a hose that shoots fire) without any controls to turn it down, or off, or even to aim it away from the hospital full of babies. Controls that were well-known as common and standard for decades before Zuckerberg was first rejected by a girl.

Facebook is entirely out of control as a social force, and PR agencies are the only thing holding back public perception.

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