Removing the Truthiness from Google

A decade ago, Stephen Colbert introduced the concept of “truthiness”, or a fact that was so because it felt right “from the gut.” When we search for information online, we are always up against the risk that the creator of a page is someone who, like Stephen Colbert’s character doesn’t trust books, because “they’re all fact, no heart.”1 Since sites with questionable or outright false facts that “feel right” often end up at the top of Google search results, librarians teach students how to evaluate online sources for accuracy, relevancy, and so on rather than just trusting the top result. But what if there were a way to ensure that truthiness was removed, and only sites with true information appeared at the top of the results?

This idea is what underlies a new Google algorithm called Knowledge-Based Trust(KBT)2. Google’s original founding principles and the PageRank algorithm were based on academic citation practices–loosely summarized, pages linked to by a number of other pages are more likely to be useful than those with fewer links. The content of the page, while it needs to match the search query, is less crucial to its ranking than outside factors, which is otherwise known as an exogenous model. The KBT, by contrast, is an endogenous model relying on the actual content of the page. Ranking is based on the probability that the page is accurate, and therefore more trustworthy. This is designed to address the problem of sites with high PageRank scores that aren’t accurate, either because their truthiness quotient is high, or because they have gamed the system by scraping content and applying misleading SEO. On the other side, pages with great information that aren’t very popular may be buried.

“Wait a second,” you are now asking yourself, “Google now determines what is true?” The answer is: sort of, but of course it’s not as simple as that. Let’s look at the paper in detail, and then come back to the philosophical questions.

Digging Into the KBT

First, this paper is technical, but the basic information is fairly straightforward. This model is based on extracting facts from a web source, evaluating whether those facts are true or not, and then whether a source is accurate or not. This leads to a determination that the facts are correct in an iterative process. Of course, verifying that determination is essential to ensuring that all the algorithms are working correctly, and this paper describes ways of checking the extracted facts for accuracy.

The extractors are described more fully in an earlier version of this work, Knowledge Vault (KV), which was designed to fill in large-scale knowledge bases such as Freebase by extracting facts from a web source using techniques like Natural Language Processing of text files followed by machine learning, HTML DOM trees, HTML tables, and human processed pages with metadata. The extractors themselves can perform poorly in creating these triples, however, and this is more common than the facts being wrong, and so sites may be unfairly flagged as inaccurate. The KBT project aims to introduce an algorithm for determining what type of error is present, as well as how to judge sites with many or few facts accurately, and lastly to test their assumptions using real world data against known facts.

The specific example given in the paper is the birthplace of President Barack Obama. The extractor would determine a predicate, subject, object triple from a web source and match these strings to Freebase (for example). This can lead to a number of errors–there is a huge problem in computationally determining the truth even when the semantics are straightforward (which we all know it rarely is). For this example, it’s possible to check data from the web against the known value in Freebase, and so if that extractor works set an option to 1 (for yes) and 0 (for no). Then this can be charted in a two-dimensional or three-dimensional matrix that helps show the probability of a given extractor working, as well as whether the value pulled by the extractor was true or not.

They go on to examine two models for computing the data, single-layer and multi-layer. The single-layer model, which looks at each web source and its facts separately, is easier to work with using standard techniques but is limited because it doesn’t take into account extraction errors. The multi-layer model is more complex to analyze, but takes the extraction errors into account along with the truth errors. I am not qualified to comment on the algorithm math in detail, but essentially it computes probability of accuracy for each variable in turn, ultimately arriving at an equation that estimates how accurate a source is, weighted by the likelihood that source contains those facts. There are additional considerations for precision and recall, as well as confidence levels returned by extractors.

Lastly, they consider how to split up large sources to avoid computational bottlenecks, as well as to merge sources with few facts in order to not penalize them but not accidentally combine unrelated sources. Their experimental results determined that generally PageRank and KBT are orthogonal, but with a few outliers. In some cases, the site has a low PageRank but a high KBT. They manually verified the top three predicates with high extraction accuracy scores for web sources with a high KBT to check what was happening. 85% of these sources were trustworthy without extraction errors and with predicates related to the topic of the page, but only 23% of these sources had PageRank scores over 0.5. In other cases, sources had a low KBT but high PageRank, which included sites such as celebrity gossip sites and forums such as Yahoo Answers. Yes, indeed, Google computer scientists finally have definitive proof that Yahoo Answers tends to be inaccurate.

The conclusion of the article with future improvements reads like the learning outcomes for any basic information literacy workshop. First, the algorithm would need to be able to tell the main topic of the website and filter out unrelated facts, to understand which triples are trivial, to have better comprehension of what is a fact, and to correctly remove sites with data scraped from other sources. That said, for what it does, this is a much more sophisticated model than anything else out there, and at least proves that there is a possibility to computationally determine the accuracy of a web source.

What is Truth, Anyway?

Despite the promise of this model there are clearly many potential problems, of which I’ll mention just a few. The source for this exercise, Freebase, is currently in read-only mode as its data migrates to Wikidata. Google is apparently dropping Freebase to focus on their Open Knowledge Graph, which is partially Freebase/Wikidata content and partially data 3. One interesting wrinkle is that much of Freebase content cites Wikipedia as a source, which means there are currently recursive citations that must be properly cited before they will be accepted as facts. We already know that Wikipedia suffers from a lack of diversity in contributors and topic coverage, so a focus on content from Wikipedia has the danger of reducing the sources of information from which the KBT could check triples.

That said, most of human knowledge and understanding is difficult to fit into triples. While surely no one would search Google for “What is love?” or similar and expect to get a factual answer, there are plenty of less extreme examples that are unclear. For instance, how does this account for controversial topics? I.e. “anthropogenic global warming is real” vs. “global warming is real, but it’s not anthropogenic.” 97% of scientists agree to the former, but what if you are looking for what the 3% are saying?

And we might question whether it’s a good idea to trust an algorithm’s definition of what is true. As Bess Sadler and Chris Bourg remind us, algorithms are not neutral, and may ignore large parts of human experience, particularly from groups underrepresented in computer science and technology. Librarians should have a role in reducing that ignorance by supporting “inclusion, plurality, participation and transparency.” 4 Given the limitations of what is available to the KBT it seems unlikely that this algorithm would markedly reduce this inequity, though I could see how it could be possible if Wikidata could be seeded with more information about diverse groups.

Librarians take note, this algorithm is still under development, and most likely won’t be appearing in our Google results any time in the near future. And even once it does, we need to ensure that we are still paying attention to nuance, edge cases, and our own sense of truthiness–and more importantly, truth–as we evaluate web sources.

  2. Dong, X. et al. “Knowledge-Based Trust: Estimating the Trustworthiness of Web Sources”. Proceedings of the VLDB Endowment, 2015. Retrieved from
  4. Sadler, Bess and Chris Bourg, “Feminism and the Future of Library Discovery.” Code4Lib Journal 28, April 2015.