I’m just going to give you a little bit of the story, and then send you to Tamino’s excellent post.

First, here is Goddard’s graphic attempting to show that global warming is not real.

See how temperatures are going down? How can that be? Note that this is average maximum temperature over time, 1918-present, in the US.

Why 1918 (the data set goes back farther)? Why the US? Also, why is he using absolute temperatures instead of the usually used anomalies? Maybe he knows something we don’t know. Or, maybe he is counting on his audience not knowing some stuff that all the experts know.

Now look at this graph, produced by climate scientist Tamino.

That’s a silly graph, isn’t it. It appears to show the mean latitude of something over time. Of what? Of the stations used to estimate temperatures. How is this relevant to the present discussion?

To learn more about climate change, see this.

This is a peer reviewed study. No kidding. Here’s the title:

Semi-supervised Content-based Detection of Misinformation via Tensor Embeddings

Abstract:

Fake news may be intentionally created to promote economic, political and social interests, and can lead to negative impacts on humans beliefs and decisions. Hence, detection of fake news is an emerging problem that has become extremely prevalent during the last few years. Most existing works on this topic focus on manual feature extraction and supervised classification models leveraging a large number of labeled (fake or real) articles. In contrast, we focus on content-based detection of fake news articles, while assuming that we have a small amount of labels, made available by manual fact-checkers or automated sources. We argue this is a more realistic setting in the presence of massive amounts of content, most of which cannot be easily factchecked. To that end, we represent collections of news articles as multi-dimensional tensors, leverage tensor decomposition to derive concise article embeddings that capture spatial/contextual information about each news article, and use those embeddings to create an article-by-article graph on which we propagate limited labels. Results on three real-world datasets show that our method performs on par or better than existing models that are fully supervised, in that we achieve better detection accuracy using fewer labels. In particular, our proposed method achieves 75.43% of accuracy using only 30% of labels of a public dataset while an SVM-based classifier achieved 67.43%. Furthermore, our method achieves 70.92% of accuracy in a large dataset using only 2% of labels.

There is a writeup on this at Think Progress by Joe Romm.

I’ve seen, and in some cases, been part of various efforts by various on line entities to clean up fake news or, more often, anti-science news or activism. What I’ve found in every case is that there is an enormous perceived shift in the bottom line for on line entities that keep out fake news vs. those that ignore it, or even encourage it.

In fact, I suspect that people who are more likely to believe, purvey, or defend fake news are also more likely to be ripped off by unscrupulous retailers. The scruples of the business are one thing, the profit levels are another. Facebook makes its money off of retailers making profits. Facebook is going to be dragged out of the fake news business kicking and screaming, if ever.

If you are like me, you live in a Facebook bubble. I do not maintain “friend” status with purveyors of fake news. The people on my Facebook feed are generally much more interested in the truth. So, it is hard to see the fake news problem as very important.

But there are all these other people who also live in a Facebook bubble. Their bubble is full of fake news. Their friends include voters, influencers, parents, maybe even teachers, who are living in that fake news bubble as well. This is a real problem. Facebook fakeness is the proximate reason that Donald Trump is president. I don’t think Mark Zuckerberg is taking that seriously, and I assume this is because he is biased by the prospect of a huge shift in profits with vs. without the heavy spice of alt-reality in the feed.

Of course you knew that these things are NOT true. But a significant number of people think they are true. These things were spread as fake news, and that fake news was bought hook, line, and sinker by a significant number of people.

What are we going to do about this?

We are going to use SCIENCE to save us, of course.

Stephan Lewandowsky, Ullrich Ecker, and John Cook (0f Bristol, UWA, and George Mason) just came out with a paper called “Beyond Misinformation: Understanding and Coping with the “Post-Truth” Era.”

This is a part of a larger project these scholars and others have been working on for some time, that springs out of the Climate Change Consensus project, via research on conspiracy ideation, and ultimately landing on the problem of fake news. From the abstract:

Imagine a world that considers knowledge to be “elitist.” Imagine a world in which it is not medical knowledge but a free-for-all opinion market on Twitter that determines whether a newly emergent strain of avian flu is really contagious to humans. This dystopian future is still just that—a possible future. However, there are signs that public discourse is evolving in this direction: terms such as “post-truth” and “fake news,” largely unknown until 2016, have exploded into media and public discourse. This article explores the growing abundance of misinformation in the public sphere, how it influences people, and how to counter it. We show how misinformation can have an adverse impact on society, for example by predisposing parents to make disadvantageous medical decisions for their children. We argue that for countermeasures to be effective, they must be informed by the larger political, technological, and societal context. The post-truth world arguably emerged as a result of societal mega-trends, such as a decline in social capital, growing economic inequality, increased polarization, declining trust in science, and an increasingly fractionated media landscape. Considered against the background lure of individual cognition that can be corrected with appropriate communication tools. Rather, it should also consider the influence of alternative epistemologies that defy conventional standards of evidence. Responses to the post-truth era must therefore include technological solutions that incorporate psychological principles, an interdisciplinary approach that we describe as “technocognition.” Technocognition uses findings from cognitive science to inform the design of information architectures that encourage the dissemination of high-quality information and that discourage the spread of misinformation.

This is good an important work, and will hopefully lead to methodologies to actually filter and fight fake information. I think this work can benefit from further consideration of points made by Shawn Otto in his recent book, The War on Science: Who’s Waging It, Why It Matters, What We Can Do About It, which provides a richer historical and sociological context than Lewandowsky et al do in this paper.

This paper hints that there is more to do in the area of stopping fake news, and suggests a “preferred approach … best described as ‘technocognition’ … the design of information architectures that incorporates principles … to “nudge” against the spread of misinformation, combined with a cognitively inspired program to educate the public and improve journalistic practices.”

I for one will welcome our new Technocogdroids as soon as they arrive!

Sadly, this paper is not available to the unwashed masses, but I promise to write up any and all further work from this team. Stay tuned!