In the War on Fake News, All of Us are Soldiers, Already!

This is intended as a supplement to my posts "A Cognitive Immune System for Social Media -- Developing Systemic Resistance to Fake News" and "The Augmented Wisdom of Crowds: Rate the Raters and Weight the Ratings." (But hopefully this stands on its own as well).Maybe this can make a clearer point of why the methods I propose are powerful and badly needed...
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A NY Times article titled "Soldiers in Facebook’s War on Fake News Are Feeling Overrun" provides a simple context for showing how I propose to use information already available from all of us, on what is valid and what is fake.

The Times article describes a fact checking organization that works with Facebook in the Philippines (emphasis added):

On the front lines in the war over misinformation, Rappler is overmatched and outgunned - and that could be a worrying indicator of Facebook’s effort to curb the global problem by tapping fact-checking organizations around the world.

...it goes on to describe what I suggest is the heart of the issue:

When its fact checkers determine that a story is false, Facebook pushes it down on users’ News Feeds in favor of other material. Facebook does not delete the content, because it does not want to be seen as censoring free speech, and says demoting false content sharply reduces abuse. Still, falsehoods can resurface or become popular again.

The problem is that the fire hose of fake news is too fast and furious, and too diverse, for any specialized team of fact-checkers to keep up with it. Plus, the damage is done by the time they do identify the fakes and begin to demote them.

But we are all fact checking to some degree without even realizing it. We are all citizen-soldiers. Some do it better than others.

The trick is to draw out all of the signals we provide, in real time -- and use our knowledge of which users' signals are reliable -- to get smarter about what gets pushed down and what gets favored in our feeds. That can serve as a systemic cognitive immune system -- one based on rating the raters and weighting the ratings.

We are all rating all of our news, all of the time, whether implicitly or explicitly, without making any special effort:

• When we read, "like," comment, or share an item, we provide implicit signals of interest, and perhaps approval.
• When we comment or share an item, we provide explicit comments that may offer supplementary signals of approval or disapproval.
• When we ignore an item, we provide a signal of disinterest (and perhaps disapproval).
• When we return to other activity after viewing an item, the time elapsed signals our level of attention and interest.

Individually, inferences from the more implicit signals may be erratic and low in meaning. But when we have signals from thousands of people, the aggregate becomes meaningful. Trends can be seen quickly. (Facebook already uses such signals to target its ads -- that is how they makes so much money).

But simply adding all these signals can be misleading. 

• Fake news can quickly spread through groups who are biased (including people or bots who have an ulterior interest in promoting an item) or are simply uncritical and easily inflamed -- making such an item appear to be popular.
• But our platforms can learn who has which biases, and who is uncritical and easily inflamed.
• They can learn who is respected within and beyond their narrow factions, and who is not, who is a shill (or a malicious bot) and who is not.
• They can use this "rating" of the raters to weight their ratings higher or lower.

Done at scale, that can quickly provide probabilistically strong signals that an item is fake or misleading or just low quality. Those signals can enable the platform to demote low quality content and promote high quality content. 

To expand just a bit:

• Facebook can use outside fact checkers, and can build AI to automatically signal items that seem questionable as one part of its defense.
• But even without any information at all about the content and meaning of an item, it can make realtime inferences about its quality based on how users react to it.
• If most of the amplification is from users known to be malicious, biased, or unreliable it can downrank items accordingly
• It can test that downranking by monitoring further activity.
• It might even enlist "testers" by promoting a questionable item to users known to be reliable, open, and critical thinkers -- and may even let some generally reliable users to self-select as validators (being careful not to overload them).
• By being open-ended in this way, such downranking is not censorship -- it is merely a self-regulating learning process that works at Internet scale, on Internet time.

That is how we can augment the wisdom of the crowd -- in real time, with increasing reliability as we learn. That is how we build a cognitive immune system (as my other posts explain further).

This strategy is not new or unproven. It is is the core of Google's wildly successful PageRank algorithm for finding useful search results. And (as I have noted before), it was recently reported that Facebook is now beginning to do a similar, but apparently still primitive form of rating the trustworthiness of its users to try to identify fake news -- they track who spreads fake news and who reports abuse truthfully or deceitfully.* 

What I propose is that we take this much farther, and move rapidly to make it central to our filtering strategies for social media -- and more broadly. An all out effort to do that quickly may be our last, best hope for enlightened democracy.

Related posts:

• The Augmented Wisdom of Crowds:  Rate the Raters and Weight the Ratings – details a broad architecture for where we should be going in social media (and digital democracy more broadly), more comprehensive and powerful than any I have seen.
• A Cognitive Immune System for Social Media -- Developing Systemic Resistance to Fake News – why we need a systemic solution to limit the spread of cognitive pathogens.
• The Tao of Fake News – the essential need for fuzziness in our logic: the inherent limits of experts, moderators, and rating agencies – and the need for augmenting the wisdom of the crowd (as essential to maintaining the intellectual openness of our democratic/enlightenment values).
• Filtering for Serendipity -- Extremism, 'Filter Bubbles' and 'Surprising Validators' – shows how this architecture addresses these specific concerns.
• Architecting Our Platforms to Better Serve Us -- Augmenting and Modularizing the Algorithm – broad solutions to technical issues of openness, transparency, regulation, antitrust, and market forces in platform architecture.
• An Open Letter to Influencers Concerned About Facebook and Other Platforms (and other posts that it refers to) – suggestions on business model incentives and regulation.

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(*More background from Facebook on their current efforts was cited in the Times article: Hard Questions: What is Facebook Doing to Protect Election Security?

[Update 10/12:] A subsequent Times article by Sheera Frenkel, adds perspective on the scope and pace of the problem -- and the difficulty in definitively identifying items as fakes that can rightly be censored "because of the blurry lines between free speech and disinformation" -- but such questionable items can be down-ranked.