The Pizzagate conspiracy theory

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Tangherlini: So the Pizzagate conspiracy theory is that Democratic politicians – along with their operatives – are part of a pedophilic, cannibalistic, satanic child trafficking ring operating out of an innocuous looking pizza parlor in northwest Washington, DC and hiding everything in underground tunnels. Right? So that is the general premise of the Pizzagate story. And people were discussing this and debating this in places like Reddit, and later on Voat. And Edgar Welch – [a] young man in North Carolina – realizes that he’s got to do something about this. So it presents the threat, but there doesn’t seem to be anybody taking action. So he decides I’m going to take action: grabs his AR-15, jumps in his pickup truck, drives up to Washington DC, enters the restaurant, tries to find the underground tunnels, and when he discovers there are no underground tunnels, he surrenders to the police.

So that’s a fascinating narrative for folklorist, right? You’re like, “Okay, we’ve got threat. We’ve got strategy, we’ve got a result.” And what I was interested in [was] what is the narrative framework that drives someone to take this kind of real world action? This is not innocuous real world action. This is a heavily armed person bursting into a family pizza restaurant and discharging his weapon. That’s really, really dangerous. Right? So, how are people taking these incredibly dangerous actions based on narrative? And what is the structure of this narrative? Is it a very complex narrative? Or is – it as I supposed – a fairly simply structured narrative of threat?

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Actual conspiracies: Bridgegate

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Watkins: Next, Doug and I asked him about how and why he and his team investigated the second kind of conspiracies: actual ones.

Tangherlini: We were then looking at this PizzaGate conspiracy theory and thought “How this thing put together?” And then, of course, we realized we had to have something to contrast it with. Now, I already knew that there was this Bridgegate thing, which I found absolutely fascinating: that there was a political payoff operation that basically relied on closing part of the George Washington Bridge to create traffic chaos in the town of Fort Lee, New Jersey, which was run by a mayor who had refused to endorse [New Jersey Governor Chris] Christie in his reelection campaign. So that wasn’t a different kind of narrative, but it was, you know … People say, “Well, how do you know, an actual conspiracy?” And well, let’s take a look at a conspiracy theory, PizzaGate, which is spurious, right? It’s fictional account. In Bridgegate, which was unfolding, and it was like, “Well, that’s an actual conspiracy.”

And fortunately, The New York Times was equally fascinated by this. And it’s always important when we’re doing machine learning work, to have some external validation data. It’s very hard to do this kind of work without something to validate your methods against. And so that’s how we wound up looking at Pizzagate and Bridgegate as two examples of on the one hand, a conspiracy theory, and on the other hand, an actual conspiracy. We didn’t really know what the structures of conspiracy theories were at that point because nobody had done consistent work on the structure of conspiracy theories. So there was a lot of discussion of conspiratorial thinking. There was a lot of discussion of who is likely to be a conspiracy theorist. There was even discussion of what are some of the main features of, at least, Anglo American conspiracy theories in the past 50 years. And that, of course, could be extended – we kind of limited our look at conspiracy theories in Europe and Anglo-America. But you could imagine that there probably are other similar phenomena in other parts of the world.

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Do conspiracy theorists “feel special”?

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Leigh: Recent research suggests that it could be the case that knowing that most people doubt a conspiracy actually makes believing in it more appealing, by fostering in the believer a sense of somehow being special. We were interested in learning why Tim thinks it is that some people tend to believe and adhere to them.

Tangherlini: I’m fascinated by some of these conspiracy theories, because I’m always wondering what the, sort of, the goal is of the conspiracy theory. So for Bridgegate, it’s very clear with the goal of the conspiracy was, right? It’s a political payback operation. They wanted [Fort Lee Mayor] Mark Sokolich to suffer, because he wouldn’t support Christie. So there’s [an] obvious end goal of the conspiracy. And so, you know, one of the conspiracy theories that we see in COVID is that Bill Gates, who’s part of some globalist cabal, wants to vaccinate everybody with his quantum dot tattoo system against COVID. So, he’s kind of manufactured COVID to get everybody to be vaccinated by this quantum dot technology. And … for … why? I don’t know why. Why? Why would anyone want to do that?

You know, at some point, that conspiracy theory if you, you know, your question is: “So why would why would people do this?” Right? They don’t have that, that sort of endgame in mind. Right? So, it’s more descriptive. This is what’s going on in the real world and I have access to this hidden knowledge, and I’m really good at interpreting what’s really going on. And so I’m going to spin this narrative for you. But there’s there’s very rarely a clear reason why Group A is manipulating B, you know, along with D and E. There’s no motivating reason for them to do that. But conspiracies – Watergate to Bridgegate – the motivation is very clear, you know, from the get-go.

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Graph theory

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Watkins: In their research, Tim and his team used graph theory, in which points representing actants – called nodes – are connected by lines – called edges – which represent the relationships between the nodes. A directed graph is a graph in which the links or edges between nodes are directional, and are represented by the lines linking the nodes ending with arrowheads. We asked Tim to tell us how he and his team went about developing the directed graphs to represent the network of interactions between the various players in the Pizzagate conspiracy theory.

Tangherlini: I had been fascinated early on by trying to come up with some representation of the interaction between characters in things like fairy tales. And I was also taken by some of the illustrations of Mark Lombardi, an artist who was really fascinated by the relationships between individuals in various power situations. One of the things we realized when we started putting together our analysis of these stories was that, taking a cue from classic narrative theory, that there are actants: people, places, things that have some sort of interaction with each other. And we can use the idea of actants as nodes and interactions as edges to draw non-trivial representations of all of the relationships within a story.

I had already started working on this kind of approach with Icelandic sagas, which often have 350 – 400 characters [which] is driving be crazy, I couldn’t keep the ball straight. And I couldn’t really keep in mind how they were interacting with each other. But as soon as I was able to draw them as actant interaction graphs, lots of patterns start to become available to you. And so this became pretty clear to us as a great way to represent the complexity of the interactions between people. It’s not just, you know, “Hillary Clinton is running for president,” right? Or, “James Alefantis owns a restaurant,” or “Tony Podesta eats pizza,” right? It’s much more complex relationships where “James Alefantis owns a restaurant that actually serves pizza, and Tony Podesta has visited the restaurant to eat pizza, and James Alefantis has helped fundraise for Hillary Clinton, who is running for president, and has held a fundraiser for Clinton for President at the pizza restaurant.” So you can’t represent that without having some more sort of complex way of representing that. Once you represent it as a network graph, of course, all sorts of fairly powerful mathematics become available to you, right? So you can now use the mathematics of graphs to start to understand the relationships and the importance of relationships. And how these networks can also be partitioned into things like communities that might tell us something because of the way we’ve drawn the graph. It might tell us something about the interactions between those nodes.

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What differentiates conspiracies from conspiracy theories

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Leigh: Jumping to the chase, we asked Tim to describe what he found about just what it is that differentiates conspiracies that, like Pizzagate, are hoaxes … from those like Bridgegate, [that] are grounded in facts hidden from the public, or from other people affected by it. We’ll hear what he had to say after this short break.

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Leigh: Here again is Tim Tangherlini.

Tangherlini: When we do this for something like Pizzagate, we find these discrete areas, which we call “domains of interaction,” these discrete areas, sort of like American politics or democratic politics, which has certain actants in it and with certain relationships. We can think of Hillary Clinton as being involved there. We can think of Barack Obama as being part of that community that have very strong relationships between them. We find another community which I call casual dining, right, which is restaurants in Washington, DC that serve things like pizza, and that families go to, and the like. And then we have another little grouping community, which we might want to call The Podesta’s because there’s John and Tony Podesta, and their brothers, and they communicate with each other. And you know, they throw dining parties, and they’d like pesto, and things like that … that’s a very discreet community. And then you also have a community that’s called, let’s call it: Satanism. And Satanism has things like child trafficking, and secret tunnels, and cannibalism. And those all form a nice little community by themselves with very strong connections to them.

And then we found there was a community which we call Wikileaks, which is all of these dumped emails. And that serves as the connective glue between these otherwise quite discreet communities, right? Otherwise, you aren’t going to associate Democratic politics with casual dining and Satanism. I’s just … it’s not going to happen, right? But the WikiLeaks dump allows you to make all of these connections. So WikiLeaks actually as a community by itself. And it’s a central community. And if we remove that community, and all of the links that come with that community, all of our discreet communities fall back into place and are no longer connected. And we thought, “Aha, that might be the connective glue.” And it is, of course, that the conspiracy theorists rely on to make these otherwise hidden connections visible between these different domains of interaction that otherwise don’t have any meaningful connections to each other. Very, very different from Bridgegate. [With] Bridgegate, all of the actants come from New York – New Jersey politics, the Port Authority, and the tensions between local and statewide government. And all of these people have multiple interactions, even outside of Bridgegate. So even if we took all of the edges that were created by Bridgegate out of the graph, the graph is still a densely connected graph. There’s nothing that breaks it apart. It’s sort of robust to deletions. When we discovered that we’re like, “Wow, that’s really cool.” And it makes sense, right? The conspiracy is deliberately hidden. And it’s embedded in this realm, this domain of New Jersey politics. Whereas Pizzagate only exists in narrative. And the only thing that’s connecting it together is the creative interpretation of these dumped emails. So those are the two things that really stuck out for us as we started doing this work.

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Gold standard graphs

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Watkins: By definition, conspiracies try to conceal the truth, and conspiracy theories are typically derived from “hidden knowledge” that can’t be shared. So Tim and his team decided to evaluate the results of both their Pizzagate and Bridgegate network graphs by comparing them to expert labeled gold standard graphs, as he describes next.

Tangherlini: When you’re working in social media, or particularly when you’re working in something like folklore, storytelling, informal storytelling: what is your comparison data? You don’t have any ground truth, right? What would be the ground truth for a story about an alien abduction, right? There’s no ground truth to that. So you have to come up with something that may not be ground truth, but maybe it’s a gold standard. And we were really fortunate that The New York Times had, in some ways, done this independently, right? They’ve come up with here’s Pizzagate, here is how it’s connected. And we thought, “Okay, we’ll use that as a gold standard,” because it’s independently derived from probably some of the same data and probably some other data, right? So it’s a representation that’s independently derived by experts who work in journalism of something as complex as Pizzagate in one of these narrative graphs formats. And the narrative graph has been kind of a Desiderata in narrative studies across many, many fields for probably half a century, or even further. Maybe going all the way back to Vladimir Propp and his morphology of the folktale: How do we represent the relationships between a large number of characters in a complex narrative?

So they had done that work. They had done it not only for Pizzagate, but they also did it for Bridgegate. So it was a perfect situation to have these two comparative data sets. So, one of the evaluations is do we match The New York Times graph? And then if we do get things that are better, you know, can we get better than The New York Times graph? Do we find things that they mentioned that they forgot to graph in their their network graph, and so that was clear for the Pizzagate. And then similarly, they have edges labeled in their Bridgegate graph, but they’re inconsistent. And so we wanted to see if we can get more consistent labeling, and so there are different ways of measuring that kind of precision. And then we came up with a mapping to do the comparison. So all of these techniques that we had to devise for the study, because they didn’t exist.

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Same actant, multiple references

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Leigh: Consider the sentence “Hillary Clinton runs a covert child trafficking ring.” Tim refers to the structure of statements such as these, what he found online, as being comprised of three components. A first argument, Hillary Clinton, a second argument, a covert child trafficking ring, and a relationship between the two: namely, that the prior runs the latter. This subject-verb-object structure is probably familiar to all of us. But with over 17,000 posts comprising almost 43,000 sentences in the Pizzagate database alone, it was especially important to train the algorithm for co-reference resolution, that is identifying all the expressions that refer to the same actant in multiple sentences.

Tangherlini: One of the problems we had was a lot of the Reddit posts refer to the previous post in the thread. And so trying to figure out what the heck they’re talking about is difficult if you just take each post independently. So we settled on one or two posts prior to it in the thread, so that we could get the co-reference resolution with multiple posts in the same thread. And that gave us much higher and much better confidence in the co-reference resolution. It’s still noisy, partly because co-reference resolution works really well on exquisitely formed text and perfectly, you know, grammatically, well-formed expressions. Which, if you’ve ever read anything on social media – particularly in some of these things, like Reddit … or we’re working a lot now with 4chan and 8chan – they don’t have the best grammar and they aren’t the most consistent. That was a problem. But by taking a larger chunk, we were able to get much higher accuracy in our co-reference resolution.

This still gives you this problem with, you know, people referring to Hillary Clinton as Hillary or “HC” or Hilary with one “L,” or Hillary with two L’s, or you know, lowercase or, you know, some strange orthographic approaches. And how do you know that those are the same? And so that’s where we rely a little bit more on word embedding, and some cosine similarity. So you see that in our, in our description of the methods, where we do both thresholding and aggregation over these subnodes. And so then once we get the subnodes – which are contextual uses of the supernodes – we were able to match those to the supernode categories. So that part is pretty finicky. But once we figured out that was the way to do it – that Hillary Clinton appears in multiple different contexts, even though it’s still Hillary Clinton – that will allow us to identify Hillary Clinton as you know, multiple subnodes under a much larger aggregated Hillary Clinton supernode. Same thing with Podesta. You know, we had Tony and John Podesta, and so those would often refer to as “The Podestas.” Right? And often it was John Podesta they were talking about, but they might just refer to him as Podesta. So how do we get that in the right place? And so that was a lot of experimentation with word embeddings similarity measures, and clustering algorithms.

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Double-clicking on network graphs 

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Watkins: The community-based graphs that Tim and his team developed present a clear overview of the main actants and the narratives and their relationships. But including the complete set of labelled edges between each subnode pair in a single visualization would result in far too many lines and labels between the many nodes to be legible. So we asked him to explain how they approach the problem of representing these associations in finer-grained detail.

Tangherlini: If you just look at, say the Pizzagate graph in The New York Times, the Bridgegate graph, you look at it and think, “Okay, well, that’s a nice high level sort of satellite view of what’s going on.” But let’s say I’m, you know, an analyst; let’ say I’m really interested in the particulars of how Hillary Clinton is considered in all of these Pizzagate posts. I want to be able to explore her at multiple levels of granularity. So the first step was: can we get this macro scale representation of what’s going on? It’s like, “Well, yes, we can.” And so that was, you know, that was kind of a eureka moment right there. And then the recognition that we actually had a whole bunch of subgraphs that were carrying a great deal of information, and a great deal of information that now had been ordered. Right? So it wasn’t a chaotic representation. It was a fairly ordered representation of, for example: the relationship between Hillary Clinton and James Alefantis, or very detailed representation of Bridgette Anne Kelly and her connections to other people in the Bridgegate conspiracy, not only at the satellite view, but dropping down to finer and finer levels of granularity. So those are all in the graph. And so the beauty of that is, you can get an overview. And then you can also drill down. You can actually drill down all the way to the phrase level, and then you can see which – and perhaps even when – that particular post was made about a very specific instance of a more aggregated relationship between two of the actants. So we think that adds a huge amount of value to these overview graphs. Now you can kind of go down to another level, and get a local community graph. And then can go all the way down to the level of the subnode and see the ego graph for, you know, one degree or two degree of Bridget Anne Kelly or James Alefantis, or the underground tunnels for that matter.

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Applications of Tim’s computational pipeline

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Leigh: The interlocking computational methods that Tim and his team developed for aggregating the reactants and relationships between them worked because they had access to a comprehensive collection of Reddit posts about Pizzagate and news articles about Bridgegate. As we live in an age characterized by an unparalleled amount of such content, Ryan and I were curious to learn what Tim sees is some potential applications of his approach.

Tangherlini: One of the cool things about our pipeline is that it’s agnostic to input. You know, if you were to take it and feed it, you know, the Washington Post and New York Times for everything about Trump and Russia, you would get a graph. And it would be very interesting to see how that graph plays out. The software is there, it’s freely available. It’s not impossibly hard to run. And so if you had access to that data, one could actually do that experiment. So that’s something I find very exciting. But that’s why we make both these detailed descriptions of our methodologies, but also our code freely available.

One of the things that we’ve been looking at is how do people read novels? And how do they remember them? How do they recount them on a review site like Goodreads? There, you’ve got a really good ground truth: you’ve got the novel itself. And so, what is it that people do when they’re writing reviews? And there’s ten of thousands of reviews of something like The Hobbit. What do they remember? What do they recount? What do they think of as important to recount if they’re writing, quote, “a book review,” which – more often than not – is some abbreviated form of plot summary.

But it’s really fun. We have this technique; I think the narrative theory behind it is is very solid. The machine learning has become refined over the the years that we’ve been working on this. We keep finding very new and interesting approaches that can help us with the the accuracy: things like these high dimensional – well, they’re low dimensional, but, you know, 780 dimensional word embeddings- the contextual word embeddings offer huge gains.

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Applications of Tim’s algorithm

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Watkins: Recently, research has suggested that well people can be inoculated against the potential harmful effects of anti-vaccine conspiracy theories once established beliefs in conspiracy theories are very difficult to correct. After talking with Tim about this a bit, we were curious to learn his thoughts on the matter.

Tangherlini: Pizzagate kind of went dormant after Edgar Welch storm the the pizza parlor and there weren’t any underground tunnels. It got new life when [an] evangelical group set up tents as part of a field hospital in Central Park. And all of a sudden, it was rumored – and this brings us the rumor into the context of conspiracy theory – that the COVID-19 lockdown was actually just to cover so that Trump and his army of … I forget what they’re called … could bring the mole-children out of the tunnels under New York, where they had been subjected to Hillary Clinton’s child trafficking for some large number of years. It’s like “Okay … that’s great!” So I mean, once you’ve kind of heard something, it’s very, very hard to discount that as a possibility.

We found this with the vaccine hesitancy. A lot of parents were coming into these mommy-blogs just interested in information. And then they start hearing all of this – or seeing all of these discussions – about “Well, you know, I’m really worried about my child, and I think they give too many shots all at once. While I’m fully on board with science, I think the science on vaccines is a little bit weak. I really just want to take this slowly. And I have a physician who’s willing to go to an alternate schedule for vaccination.” So now, you’re a new parent, and all of a sudden you’re like, “Oh, people are a little bit worried about these. Oh, and there’s something called an alternate schedule.” Now that I’ve said that it’s out there, and it’s very hard to un-hear what I’ve said.

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Spreading conspiracy theories

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Leigh: Just as blogs have superseded the traditional media as a news source for many people, social media has expanded the reach of interpersonal opinions far beyond the proverbial office water coolers and barbershops. As we learned from our guests, Souroush Vousougi in episode 20 of Parsing Science, false news disseminates differently than true news on Twitter. So, to close out our conversation, Ryan and I were interested in learning Tim’s thoughts as to how and why the internet can be used to spread conspiracy theories further than ever before.

Tangherlini: So, I think what social media gives us is what I call both amplification – so you can amplify your message very, very loudly – and [it] also gives us velocity: direction and and speed. And so, both of those things – amplification and velocity – are new features of a storytelling environment that, you know, has been documented going back hundreds of years. So it’s not as if we’ve invented conspiracy theories. And it’s not as if the idea that people are taking real world action on these is something new. People have done this even in the past. You know, if we think it’s the past hundred years, we’ve got lots of genocide that is based on storytelling, right? The Hutu and Tutsi genocides are often based on historical narratives that set one group up as an outside group and often dehumanize them and represent them as a threat. And so, then it becomes this question: “Here’s the threat, what are we going to do about it?” And the most totalizing response to that is, of course, eradicate the threat. Humans I think, are predisposed to telling stories about threat and figuring out ways to deal with that threat. And that’s had really tragic and devastating and global impact. So I fear that a hundred years from now, it’s it’s just going to – in some ways – blur into, you know, the thousands of years of human storytelling.

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Links to manuscript, bonus audio and other materials

Leigh: That was Tim Tangherlini discussing his open-access article, “An automated pipeline for the discovery of conspiracy and conspiracy theory, narrative frameworks, Bridgegate, Pizzagate, and storytelling on the web,” which he published with four co-authors on June 16, 2020 in the journal PLOS One. You’ll find a link to their paper at pricingscience.org/e81, along with transcripts, bonus audio clips, and other materials that we discussed during the episode.

Watkins: You probably already know about Parsing Science’s website and our toll free message line 1-800-X-P-L-O-R-I-T. But did you know that we also tweet news about the latest developments in science, including many brought to our attention by listeners like you? You can follow us @parsingscience. And the next time you spott a science story that fascinates you, let us know: we might just feature the study’s researchers in a future episode of the show.

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Preview of next episode

Leigh: Next time, in episode 82 of Parsing Science, we’ll talk with Nicole Barbaro from Western Governors University Labs about her research into whether spanking causes negative developmental outcomes. You’ll be surprised to find that yes, it can. But those effects are probably much smaller than you might think.

Nicole Barbaro: Children that have, let’s say a family history of problem behaviors: even if you put those children into world’s best parents household, they’re are going to be a little bit more sensitive to harsh parenting.

Leigh: We hope that you’ll join us again.

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