After Donald Trump’s recent election as US president, many people have tried to blame fake news for the results. They made Trump look better in the elections. They ‘villified’ Hillary. Fears of non existent crimes boosted his support.

Basically, the attitude is that fake news made Trump popular by emphasing right wing talking points and misconceptions and somehow swayed the election as a result.

So because of this, everyone is talking about a fake news crackdown. A magical way to seperate real and fake news and make sure the latter never ends up being popular on Google, Facebook or other such internet sites.

In theory, this is a noble goal. After all, an informed electorate is useful for fair elections, right?

Well, yes. But stopping fake news is harder than it sounds.

Why? Because as I’ll show below, just about every method you can think of to stop fake news is either fatally flawed or likely to run into issues down the line. Human editors, algorithms, black/white lists… they’re all the same and they’re all going do sod all to stop fake news being shared or monetised online.

So let’s go through them one by one shall we? Starting with the most often considered choice, human editors…

Human Editors

Because after looking at Snopes and other fact checking sites, this seems to be the most common idea people have to fix the fake news issue. Basically, you get a load of staff and tell them to do nothing but sort through news articles and figure out which are real and which aren’t.

And as you can see, there are some clear positives to this approach.

Pros:

More Accurate: Like how human editors can figure out whether something is real a lot better than machines can. After all, the former can look around the site, read any disclaimer notices in the footer or about pages and cross reference the news with other, more obviously reliable sites on the same topic to see whether anyone else is saying the same thing.

Harder to Game: They’re also harder to trick than machines are, especially if they’re mostly anonymous and make decisions as a group whenever an article is too convincing for a single person to determine the validity/accuracy of it.

However, human editors do have quite a few flaws too. Such as editorial bias…

Cons:

Editorial bias. Cause at the end of the day, humans are humans. They will be biased towards one political stance or social view or another, and their decision making skills will be affected as a result. I mean, just look at why people post these obviously fake articles. Because they support their own existing beliefs. And people tend not to question arguments or opinions that prove them right.

And in case you think it’s only political biases that are problematic here… well, you’d be wrong. People are biased based on sorts of things outside of political stance/party support. Like say, gender, age, race/ethnicity, nation, religion or social class among other things. Any of those things could cause an editor to take one article seriously and treat a different one with kid gloves.

Which means that Facebook would either need to:

1. Hire editors who are completely neutral in regard to every aspect of their personality and social upbringing (this is virtually impossible)

2. Or hire a very large, very diverse group of editors who each don’t make a single decision on their own in case it’s biased. Yeah, that’s not gonna work.

And if they did try any of that? Well, then they’d have to somehow attract candidates/employees from all over the world rather than just where their offices are based. Because if every editor/fact checker has to be located in Facebook’s headquarters, then guess what? They’ll probably attract mostly young, white Democrat voters from middle class or above upbringings. Good luck avoiding political bias against conservative viewpoints with that group of employees!

Cost: What’s more, these fact checkers/moderators would also be expensive. After all, you’re paying someone a wage they can live on in (likely) one of the most expensive cities in the world. No wait, that’s not quite right.

You’re paying hundreds of people a wage they can live on in one of the most expensive cities in the world, unless you literally want to work your staff into the ground. Which at around $60,000 a year (apparently about average for social media type work in the Bay area), could add to them a good ten million plus dollars a year if you hire a large group of checkers.

What’s more, it’s not even work you can outsource too well. I mean, how is a guy millions of miles from North America supposed to figure out whether a Trump or Clinton story is accurate? They’re not. Unless they’ve been following the election really closely, they won’t have a clue what’s real and what isn’t here.

So no, you’ll need people with at least a decent amount of exposure to US culture. Oh, but wait, aren’t we forgetting something here?

Yeah. All this argument assumes you’re only fact checking Trump/election 2016 stories.

But you’re not. You’d also need to fact check stories related to every other election and major event in the world. After all, you can’t assume the French or Germans will be any better at checking if a story is fake, right?

No, not really. So you also need teams to check information about the elections in every European country, every Asian country Facebook or Google are accessible in, Australia, Canada, various places in the middle east, Africa and all of central and South America in addition to the US. That fact checking team is getting bigger by the day, isn’t it?

Yeah. And so is the cost of running it. What started as a $12 million deal is now likely to become a $50–60 million one. Which brings us to the next issue…

Inpracticality: Namely, how impractical the whole idea is. At the end of the day, you can’t afford to pay people to check every single article that gets posted on a mainstream social media site. Human editors work just don’t scale.

Slow speed: And they’re slow too. So hey, you’re now in a situation where the ‘analysis’ is likely to take anywhere between 10 minutes (for an obvious fake news site) to an hour or two (for a less obvious/simply super partisan one). Either way, it means your fake news warning is gonna be too late to stop the news being spread.

So human staff are out. How about automating it?

Automated Article/Source Checking

Well, it turns out that’s not really much better either. It has a few positives, such as…

Pros:

Cost: It being cheaper to get a team of programmers to code and maintain a system rather than manually check every article.

Editorial Bias: And editorial bias being reduced to the lack of human input.

More practical: As well as the system being quicker and much more practical. But it still has significant issues too. Such as…

Cons:

Complexity: Because detecting whether an article is real or fake is not easy for a machine. Think about it for a moment. What would it require to accurately and automatically check if a story is fake?

Well, you’d need some way to scan the content, then a way to check to see whether other (more reliable?) sources are covering the same thing, and perhaps a way to tell whether the site itself is a joke (like a disclaimer on the about page) and maybe even a way to figure out the characteristics of ‘fake news’ and how it can be differentiated from the real stuff.

That’s pretty hard by itself. However, you’ve then also got to watch out for false positives and negatives too, since you don’t want your system accidentally classified the rest of the media as fake or treating an obviously satirical site as real because it doesn’t match the others of its kind.

Easily Gamed: But here’s the last (and most crucial) issue with algorithms for stuff like this.

People will exploit them to hell. Seriously, look at what happened the last time we had a system that helped decide who’s work appeared to a large audience!

Don’t know what I mean?

Well, here’s a hint; it was called ‘page rank’. It was part of Google’s algorithm to decide what content would appear higher in their search results.

Guess what? It was very quickly abused to hell, to the point the old ‘more links to a page causes it to rank higher’ concept was minimised over time and the value removed from Google’s toolbars for being meaningless.

And yet because Google’s algorithms are still automatic, this sort of gaming still goes on. It’s called ‘SEO’.

People got so interested in beating the algorithms that they turned SEO into a 65 billion dollar industry. That thousands of blogs, news sites, forums and conferences now exist dedicated to nothing more than ‘how to rank well n Google’.

With automated fake news detection, this will only happen again. Fake news detection will end up becoming yet another billion dollar industry filled with scammers, astroturfing, manipulative content and ‘verification’ schemes.

But how about community moderation you might ask? Could that be better?

Community Moderation:

Again, no. It certainly has some positives, mostly the following ones:

Pros:

Practical: You can outsource the process to your site/service visitors, and use their decisions to judge whether a source is reliable or not.

More Accurate: Than machines anyway, since you’re dealing with humans doing research rather than machines going through a checklist.

Cheap: Some people will do this stuff for free or very low pay.

Cons:

But then it has a lot of issues too. Why? Because communities are their own kettle of fish. By relying on the community to verify fake news, Google or Facebook would basically turn into Reddit, which has issues such as being…

Not Too Accurate: Because hey, most of your users are (to put it nicely) morons. They’re the same people who already fall for fake or heavily politicised news, except now being asked to figure out whether said news is real or not.

This would be a disaster just waiting to happen. Trust me here, look what happened when a Stanford study asked kids and college students to detect fake or questionable news.

82% of middle schoolers couldn’t distinguish between an ad labelled sponsored content and a real news story, and many college students could be persuaded a news item was real because the site it was on had ‘high production values’.

This trend is probably not gonna improve with the population at large. For a lot of average Joes, it’s just too hard to detect whether an article is fake or misleading. And like automated systems and SEO it’s still…

Easily Gamed: As anyone who’s used Reddit or Voat will tell you. There are shills everywhere on sites like this, and with the possibility of getting your content to millions of people on the line, there will be even more with a user based news trust system like this one.

There will also be liars trying to use ‘negative SEO’ like tricks against their enemies, especially where competing news sites are concerned. So some news sites will probably try and destroy their opponents’ reputations by repeatedly marketing their work as fake to get them banned or filtered out.

And then there’s just plain disgruntled visitors, political rivalries and other drama having an effect. Think it was bad enough between Trump and Clinton supporters prior to the election? Well, imagine what it’ll be like when these people are trying to knock their opponents’ sites out of Google/trying to get them blocked from Facebook. It’ll be a big back and forth between liberals trying to downvote/filter out conservative news and conservatives doing the same for ‘liberal’ news.

Not to mention people with a simple grudge against the site. I mean, my site had that happen with Web of Trust before. I banned someone, so them and their friends got together and basically flooded its WOT page with negative ratings. So now anyone who viewed my site with the extension installed thought it was an attack page or something.

But yeah, imagine that plus say, CNN or the New York Times. All they’d need to do is ban a few trolls, and suddenly all their coverage is filtered out as ‘fake’.

In addition, this system…

Can Cause Confusion: Since there will be two sides voting at any one time, so it’s very possible a news story would flip between ‘real’ and ‘fake’ every ten minutes or so. Good luck using a system like that, especially if the internet’s ‘consensus’ changes more often than the British weather.

So how about blacklists or whitelists?

Source White/Blacklisting (manual):

Well, as you can see below, they’re not a good idea either. A manual one has a few minor advantages like being…

Pros:

Quick: Because checking against a list is always quicker than algorithmically looking into a specific page or article. It’s literally one database query vs a much deeper analysis.

And if it’s a manual system like mentioned in the title, it’s also moderately…

More Accurate: Since human staff are going to be better at fact checking than machines and will probably have at least some idea as to whether a source is reliable or not.

Cons:

Unfortunately though, this type of system is heavily flawed. Why? For a multitude of reasons really, the most important being that the system is…

Non Flexible: Beause sources aren’t always reliable or unreliable. They differ based on what content they’re covering and the situation involved.

For example, BuzzFeed has both great articles and terrible articles on the same domain, and a lot of gaming news sites are wrong about some things and right about other things on the same site as well. In addition to that, they’re also…

Outdated: Since a good news can become bad or vice versa simply based on who owns and controls it. Imagine for example, a left/right wing politician buys a newspaper, or a company like News Corp takes over a previously respected online publication. It’s quite likely the content covered and the quality of the reporting would change, and a blacklist/whitelist would have update quickly enough to take this into consideration.

Oh, and make sure only to mark new/edited articles as different, since it’d still possible that old ones would remain reliable/unreliable even after the takeover/rush for ad clicks kicks in.

All these also imply to automated white/blacklisting, as seen below:

Source White/Blacklisting (automated)

Except with the reliability and accuracy of the service being far less. Either way, an outdated, unreliable list is not the way forward and probably shouldn’t even be considered at this point.

But what about outsourcing to third parties? Maybe that might help?

3rd Party Fact Checking (aka, get Snopes to do it)

Well, not quite. It’s certainly got its advantages, such as the system being…

Pros:

More Accurate: Since hey, sites like Snopes have a history of fact checking content, and have proven over time to be mostly trustworthy. That’s an improvement on the other methods as far as accuracy is concerned.

But at the same time, it’s also genuinely flawed as a concept. Why?

Cons:

Because in a nutshell, there’s no large scale ‘fact checking’ service. There’s no billion dollar corporation working on checking whether a news site is reliable or whether a specific story or rumour is accurate, and that causes a barrage of different issues…

Cost: Like the cost required. Snopes isn’t exactly a giant corporate operation with thousands of staff, it’s a team of about 10 people working on a regular basis. But if they were going to work with a company like Facebook or Google, they’d need a crap load more resources, including a much larger staff team, proper offices, better hosting and all the technology need to integrate their systems in a convenient way.

And that cost would be passed onto the site needing the fake news checking services.

That’s not all either. Oh no, it’d also have problems with its…

Slow Speed: Since sites like Snopes do actual fact checking rather than having some bot do it for them. This would make for a very slow process, especially where the likes of Google or Facebook and millions of articles are concerned.

It’s also got issues with…

Impracticality: Because the likes of Snopes or their competitors couldn’t keep up with the demand from Google or Facebook, let alone from both companies at once. Even reeling in About Urban Legends, Museum of Hoaxes, Skeptic’s Dictionary, Bad Science, Bad Astronomy, FactCheck.org, PolitiFact, TruthOrFiction and HoaxSlayer (at the same time) couldn’t keep up with demand.

In other words, as I said earlier, you’d need a big service dedicated to checking whether stories are true. That just doesn’t exist in the world today.

Reliance on 3rd Party: Finally, if the site used shut down, then the big corporation would be up a creek without a paddle. It’s not a big market, there are fairly few competitors to turn to out there, and it’d be extremely difficult to switch over the service/API used without the users noticing/the submissions going a while without being checked.

So What’s the Solution?

I honestly don’t know what the solution really is here. Every possibility can work well in some situation or another, but then completely fall about when merely a few small things are changed. As far as practicality goes, I don’t think there is a solution. Well, maybe short of hoping AdBlock eventually makes the fake news business unprofitable.

Yeah… good luck with that one.

There’s also strong AI and machine learning possibilities, but that’s not quite far enough along to be a solution here yet. Might stop ‘fake news’ at the 2056 elections though!

In conclusion, there is no ‘easy’ way to detect or stop fake news, and any methods used to attempt it should be cleverly analysed to make sure they don’t have any unintentional effects. Otherwise, the ‘cure’ really could be worse than the disease with this problem.