Note: this is cross-posted on the CUNY Tow-Knight Center for Entrepreneurial Journalism site.
Over the last decade there’s been a substantial growth in the use of Fact-Checking to correct misinformation in the public sphere. Outlets like Factcheck.org and Politifact tirelessly research and assess the accuracy of all kinds of information and statements from politicians or think-tanks. But a casual perusal of these sites shows that there are usually only 1 or 2 fact-checks per day from any given outlet. Fact-Checking is an intensive research process that demands considerable skilled labor and careful consideration of potentially conflicting evidence. In a task that’s so labor intensive, how can we scale it so that the truth is spread far and wide?
Of late, Politifact has expanded by franchising its operations to states – essentially increasing the pool of trained professionals participating in fact-checking. It’s a good strategy, but I can think of at least a few others that would also grow the fact-checking pie: (1) sharpen the scope of what’s fact-checked so that attention is where it’s most impactful, (2) make use of volunteer, non-professional labor via crowdsourcing, and (3) automate certain aspects of the task so that professionals can work more quickly. In the rest of this post, I’ll flesh out each of these approaches in a bit more detail.
Reduce Fact-Checking Scope
“I don’t get to decide which facts are stupid … although it would certainly save me a lot of time with this essay if I were allowed to make that distinction.” argues Jim Fingal in his epic fact-check struggle with artist-writer John D’Agata in The Lifespan of a Fact. Indeed, some of the things Jim checks are really absurd: did the subject take the stairs or the elevator, did he eat “potatoes” or “french fries”; these things don’t matter to the point of that essay, nor, frankly, to me as the reader.
Fact-checkers, particularly the über-thorough kind employed by magazines, are tasked with assessing the accuracy of every claim or factoid written in an article (See the Fact Checker’s Bible for more). This includes hard facts like names, stats, geography, and physical properties as well as what sources claim via a quotation, or what the author writes from notes. Depending on the nature of the claim some of it may be subjective, opinion-based, or anecdotal. All of this checking is meant to protect the reputation of the publication and of the writers. To maintain trust with the public. But it’s a lot to check and the imbalance between content volume and critical attention will only grow.
To economize their attention fact-checkers might better focus on overall quality; who cares if they’re “potatoes” or “french fries”? In information science studies, the notion of quality can be defined as the “value or ‘fitness’ of the information to a specific purpose or use.” If quality is really what we’re after then fact-checking would be well-served and more efficacious if it focused the precious attention of fact-checkers on claims that have some utility. These are the claims that if they were false could impact the outcome of some event or an important decision. I’m not saying accuracy doesn’t matter, it does, but fact-checkers might focus more energy on information that impacts decisions. For health information this might involve spending more time researching claims that impact health-care options and choices; for finance it would involve checking information informing decisions about portfolios and investments. And for politics this involves checking information that is important for people’s voting decisions – something that the likes of Politifact already focus on.
Increased Use of Volunteer Labor
Another approach to scaling fact-checking is to incorporate more non-professionals, the crowd, in the truth-seeking endeavor. This is something often championed by social media journalists like Andy Carvin, who see truth-seeking as an open process that can involve asking for (and then vetting) information from social media participants. Mathew Ingram has written about how platforms like Twitter and Reddit can act as crowdsourced fact-checking platforms. And there have been several efforts toward systematizing this, notably the TruthSquad, which invited readers to post links to factual evidence that supports or opposes a single statement. A professional journalist would then write an in-depth report based on their own research plus whatever research the crowd contributed. I will say I’m impressed with the kind of engagement they got, though sadly it’s not being actively run anymore.
But it’s important to step back and think about what the limitations of the crowd in this (or any) context really are. Graves and Glaisyer remind us that we still don’t really know how much an audience can contribute via crowdsourced fact-checking. Recent information quality research by Arazy and Kopak gives us some clues about what dimensions of quality may be more amenable to crowd contributions. In their study they looked at how consistent ratings of various wikipedia articles were along dimensions of accuracy, completeness, clarity, and objectivity. They found that, while none of these dimensions had particularly consistent ratings, completeness and clarity were more reliable than objectivity or accuracy. This is probably because it’s easier to use a heuristic or shortcut to assess completeness, whereas rating accuracy requires specialized knowledge or research skill. So, if we’re thinking about scaling fact-checking with a pro-am model we might have the crowd focus on aspects of completeness and clarity, but leave the difficult accuracy work to the professionals.
#Winning with Automation
I’m not going to fool anyone by claiming that automation or aggregation will fully solve the fact-checking scalability problem. But there may be bits of it that can be automated, at least to a degree where it would make the life of a professional fact-checker easier or make their work go faster. An automated system could allow any page online to be quickly checked for misinformation. Violations could be flagged and highlighted, either for lack of corroboration or for controversy, or the algorithm could be run before publication so that a professional fact-checker could take a further crack at it.
Hypothetical statements, opinions and matters of taste, or statements resting on complex assumptions may be too hairy for computers to deal with. But we should be able to automatically both identify and check hard-facts and other things that are easily found in reference materials. The basic mechanic would be one of corroboration, a method often used by journalists and social scientists in truth-seeking. If we can find two (or more) independent sources that reinforce each other, and that are credible, we gain confidence in the truth-value of a claim. Independence is key, since political, monetary, legal, or other connections can taint or at least place contingencies on the value of corroborated information.
There have already been a handful of efforts in the computing research literature that have looked at how to do algorithmic corroboration. But there is still work to do to define adequate operationalizations so that computers can do this effectively. First of all, we need to define, identify, and extract the units that are to be corroborated. Computers need to be able to differentiate a factually stated claim from a speculative or hypothetical one, since only factual claims can really be meaningfully corroborated. In order to aggregate statements we then need to be able to match two claims together while taking into account different ways of saying similar things. This includes the challenge of context, the tiniest change in which can alter the meaning of a statement and make it difficult for a computer to assess the equivalence of statements. Then, the simplest aggregation strategy might consider the frequency of a statement as a proxy for its truth-value (the more sources that agree with statement X, the more we should believe it) but this doesn’t take into the account the credibility of the source or their other relationships, which also need to be enumerated and factored in. We might want algorithms to consider other dimensions such as the relevance and expertise of the source to the claim, the source’s originality (or lack thereof), the prominence of the claim in the source, and the source’s spatial or temporal proximity to the information. There are many challenges here!
Any automated corroboration method would rely on a corpus of information that acts as the basis for corroboration. Previous work like DisputeFinder has looked at scraping or accessing known repositories such as Politifact or Snopes to jump-start a claims database, and other work like Videolyzer has tried to leverage engaged people to provide structured annotations of claims. Others have proceeded by using the internetas a massive corpus. But there could also be an opportunity here for news organizations, who already produce and have archives of lots of credible and trustworthy text (e.g. rigorously fact-checked magazines), to provide a corroboration service based on all of the claims embedded in those texts. Could news organizations even make money by syndicating their archives like this?
There are of course other challenges to fact-checking that also need to be surmounted, such as the user-interface for presentation or how to effectively syndicate fact-checks across different media. In this essay I’ve argued that scale is one of the key challenges to fact-checking. How can we balance scope with professional, non-professional, and computerized labor to get closer to the truth that really matters?