We Asked an Expert if Memes Could Determine the Outcome of the Presidential Election

At one point during the early stages of the shitshow we're calling the 2016 Presidential Elections, a friend of mine claimed that winner of Democratic primaries would be "decided by memes." He suggested that Bernie Sanders had the leg up in that department. While Hillary Clinton's delegate dominance is proving my friend's theory to be a bit flawed, it's undeniable that memes have become omnipresent in this campaign cycle.

From the "Bernie or Hillary?" memes to the Bernie Sanders's Dank Meme Stash Facebook group (just shy of 420k members), the visual punchlines and internet inside jokes have featured all the frontrunners in both parties, and likely gotten more people interested in the campaign. Through the accessibility and share-ability of memes, it wouldn't be wrong to say they've offered the public a nuanced (albeit passive) way to participate in political campaigns.

But memes aren't just the lifeblood of Imgur and stoners who love to #bern. They're also impacting data science, and have even become the focus of government-funded academic research. In 2014, researchers at Indiana University received several grants, including close to a million dollars from the National Science Foundation, to start the Truthy Project, which is dedicated to studying the spread of information through socio-technical information networks and analyzing how and why things go viral. It's also attempting to make social media data more accessible to researchers.

Truthy is spearheaded by informatics and computer science professors Alessandro Flammini and Filippo Menczer, who is also the Director of the Center for Complex Networks and Systems Research at Indiana University. The research team aims to eventually make its data public and open-sourced. In the meantime, we got on the phone with Menczer to learn more about the Truthy Project and discuss whether memes could actually change the outcome of this year's election.

VICE: What were you looking for when you started Truthy, and what did you find?
Filippo Menczer: Since 2009, we've been working on studying the diffusion of memes and information on social media, especially on Twitter because we were fortunate to have access to a lot of data from Twitter. We have several grants supporting this research, and some of it is very much towards building systems. For example, to visualize or analyze how information is spread from one person to another, studying what makes memes go viral, and the competition among different memes for the finite attention of users. Is it the structure of the network or is it another factor that make these memes go viral? These are general things we wanted to address with Truthy about how social media affects the spread of information, and also the spread of misinformation.

What else have you worked on since starting the project?
We also built models where we tried to replicate very simplified scenarios in which people use social media. We look at the predictions of these models, like which memes go viral, or how two different opinions compete with each other, and then we see if we can reproduce some empirical patterns from the data. We've looked at if people can distinguish real humans from bots on Twitter, and we've had some success with that.

More recently, we've been studying whether you can detect whether a meme or a hashtag is being promoted by advertisement, versus information that is being spread organically.

What exactly makes a meme go "viral"?
That's a very interesting question. A lot of people have looked at this issue and found that different factors may affect the virality of a meme, such as time of day, color, and pictures included. But we were curious to know if that was, in fact, a causal relationship.

We tried to explore this question in a theoretical way by building some models that simulate people using Twitter or Facebook, where something pops up on a user's screen and you can retweet it, skip it, or create new content that would go to your friends or followers.

What did you find from the experimentation?
What we found in this model was that something almost always went viral when there was an underlying structure of a social network where the users had a finite amount of attention. Yet, in this model, the things that went viral were no different than the things that did not go viral.

So sometimes it is inevitable that things will go viral due to the structure of the social network and because of our finite attention. To say, "This is the thing that makes a meme go viral," you have to prove that it's not just a coincidence. The things that go viral may have some things in common, but that doesn't mean they're the reason why they went viral.

One feature that we did find correlated with virality is whether a meme, in its early stages, spreads through multiple communities.

Meaning multiple platforms, like from Twitter to Facebook?
Not exactly. For example, say you have high school friends, fellow journalists, and people who like tennis who follow you on Twitter, and these are your communities, or commonalities. If a meme, very early on, spreads through many of these communities, it's much more likely to go viral.

Right, so the exposure and interaction with the meme is what makes it go viral, not necessarily the content.
Yes. If [a meme] spreads to 10,000 people very early on who are all in one community, then it's less likely to go viral. But if it goes through 100 people who are all from different communities, and they interact with it, it's much more likely to go on and reach a million people.

How do you think memes and political engagement on social media are affecting this presidential election or the public's perception of it?
In 2012, we worked on a research paper where we looked at the structure of the networks formed by people tweeting and retweeting about words associated with the 2010 midterm congressional elections. This is a slightly dated analysis, but I can summarize something interesting that we found. I assume that it still holds true.

We looked at diffusion networks through Twitter, where a "node" is a user and a "connection" means that a particular meme has been transferred from one user to another person, either through mention or retweet. We did this for all hashtags that were used to talk about politics in 2010.

What'd you find?
We found that there was huge polarization and segregation between the two big communities. The liberals retweeted the liberals and the conservatives retweeted the conservatives, but there were very few liberals who retweeted conservatives, and vice-versa. We use this term "echo chambers" to describe how people are exposed to information that is aligned with their preexisting beliefs. We're very interested in the fact that these echo chambers still exist. For example, if you have a friend on Facebook who has a different opinion, you can just unfollow them, right? So that makes it very easy for those echo chambers to form, and these may very strongly effect the conversation about politics on social media.

Another thing that we found to be true was that each of these two networks, both the liberal network and the conservative network, are very dense. They are structured in a way that makes it very easy for messages to propagate extremely fast. With social media, it is easier to simply disregard people with different opinions.

Based on your previous research in 2010, and the recent research of your colleagues, do you think that the virality of a meme could be directly correlated to a candidate's success?
There are some colleagues at Indiana University who found that you could use Twitter as a relatively accurate poll. There was a clear correlation between the number of tweets about the politician and the number of votes that they received. So that would suggest that yes, if there are memes about candidates that are more popular online, then it's fair to assume that candidate may be more likely to win.

For more on the Truthy Project visit their website.

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This interview has been edited for length and clarity.