Identifying and Estimating the Ideologies of Twitter Pundits

By Jon Green (@_Jon_Green)

In the grand scheme of things, the direct effects Twitter has on public opinion are likely quite limited. Twitter claims a relatively modest market share in terms of social media platforms (it is much smaller than Facebook, for instance) and many of its users don't use it for politics. As one influential Twitter user put it, somewhat polemically, in the midst of a debate over Twitter politics in the 2016 primary, nobody is on Twitter.

But while it may generally be the case that, for most intents and purposes, nobody is on Twitter, in a narrower sense everybody is on Twitter. Specifically, everyone who traffics in political news or opinion for a living is more or less professionally obligated to engage with their fellow politicos on the microblogging platform. If we think that what happens among this chattering class is important for politics, and there is some academic research arguing that it is, then it is useful for us to try and get a handle on two things. First, who comprises the population of politically influential Twitter users -- referred to here as "pundits" in a less derogatory sense than the word is generally used? Second, how are these pundits distributed ideologically?

Who is a pundit?

There is no clear definition of who counts as a pundit. However, on a user-by-user basis there is a general sense of who fits the bill and who doesn’t. We know a pundit when we see one. There are also a few general principles that we can use to say for sure who isn’t a pundit -- elected officials, for instance, are clearly a different sort of Twitter user than people who are paid to produce political news and opinion.

In order to identify who counts as a pundit on Twitter, I used a series of “snowball samples” that use the wisdom of crowds to make classifications based on categories that don’t have clear definitions. For the left, center, and right, I started with a small list of accounts who I considered to be representative cases of the sort of user I was looking for, then used Missouri professor Mike Kearney’s rtweet package to search for all of the Twitter lists these accounts are on, and then filtered down to lists that had particular keywords in their titles (such as “liberal,” “socialist,” or “democrat” for the list of left pundits, for example). Then, I used rtweet to identify all of the other users on those lists to get a sense of who else, apart from my representative cases, Twitter users thought were similar enough to include on lists of accounts they turn to for political tweets. This list-based snowball sampling method was inspired by this short paper.

Once this process was complete, I filtered the samples down to only accounts that were included on at least ten percent of the total number of lists identified in each sample. In other words, if only one or two users included a given account on a political list, but the broader crowd did not agree that this was a political account worth listing, they didn’t make the cut. Additionally, in cases where pundits identified in the centrist snowball sample were duplicated in the left or right snowball samples, they were removed from the centrist sample. I also manually removed accounts that belong to elected officials, clearly political figures (such as Donald Trump, Jr., Rudy Giuliani, Michelle Obama, and Eric Holder), and organizational accounts (such as NPR Politics, MSNBC, and Mother Jones). The final list of pundits includes 445 users, which passes the smell test for how many members of the “chattering class” can be reasonably expected to influence political discourse on regular basis. Of these, 276 were identified in the centrist sample (though as we’ll see below, there is plenty of heterogeneity in this group), 73 in the left sample and 96 in the right sample.

Estimating pundit ideology

Much in the same way that “pundit” lacked a clear definition going into this analysis, but we more or less know one when we see one, we currently only have a general sense of how pundits relate to each other in ideological space. As in, it is clear that some pundits are more liberal and others are more conservative, and that there are important distinctions within these general ideological categories, but there isn’t a well-established way of quantifying these relationships.

In order to place pundits in quantifiable ideological space, I drew inspiration from this post from data scientist Jason Timm that examines similarities between the outbound links Senators tend to share in their tweets. While this exact methodology would not work for pundits, who don’t necessarily convey as much ideological information in the outbound links they share because a) they share more straight news articles and b) pundits who work for the same outlet share links from that outlet very frequently, even if they don’t share similar ideologies (think Bret Stephens and Paul Krugman, who both share a lot of links from the New York Times). However, we can use a different measure of similarity to estimate pundit ideology: similarities in who they follow.

In order to estimate pundit ideology, I again used rtweet to collect all of the accounts that each pundit follows -- up to 5,000 accounts (the maximum returned in a single API call). I then filtered that list down to accounts that were followed by at least two pundits. From there, create matrix where each row is a followed user and each column is a pundit account; the cells are filled in with 1s if the pundit follows that account, and 0 otherwise. Next, I estimate the cosine similarity between each pundit, and use classical multidimensional scaling to identify the two eigenvectors that best represent the similarity matrix. This is a fancy way of saying that I identified the two dimensions that best explain the overlap in who each pundit follows.

The results look like this:

punviz_logo.png

The labeled pundits in the above chart indicate how well the combination of snowball sampling and follow similarity performs in identifying pundits on Twitter and placing them in ideological space. There is a clear left/right dimension, with most of the accounts identified in the “centrist” snowball sample falling in between those who were identified in the left and right samples. The plot also shows a second dimension, which looks to me like an establishment/outsider dimension but could reflect some other pattern.

Plotting these against each other shows distinct clusters of pundits. In the top left, for instance, are Jacobin Magazine assistant editor Alex Press and Chapo Trap House host Matt Christman, who are both very far to the left and very far removed from what we’d normally think of as “establishment” politics. This is in contrast to figures such as Neera Tanden, the president of the Center for American Progress, who is widely recognized on Twitter as being less far to the left and less far removed from the establishment. In between these groups are pundits such as Matt Yglesias and Elizabeth Bruenig, who are squarely on the left but also write for mainstream publications Vox and the Washington Post, respectively.

The center identifies a number of political reporters and analysts who aim to either be heterodox or ideologically neutral. Jake Tapper, for instance, is known for being doggedly centrist, to the frustration of his followers on both the left and right. Bret Stephens, a self-styled Never Trump conservative, also follows users in a pattern that places him to the left of more conservative users -- such as National Review editor Charles CW Cooke and Federalist co-founder Sean Davis, who tend to be both more conservative and farther removed from mainstream Twitter discourse.

In the upper right corner of the plot are extremely conservative accounts who have little use for and are often actively hostile toward mainstream discourse -- such as Townhall contributor Kurt Schlichter and Daily Wire founder Ben Shapiro. This corner also includes members of the alt-right, such as InfoWars contributor Paul Joseph Watson and YouTube provocateur Stefan Molyneux.

We can explore these ideological dimensions further by examining their full distributions by pundit class. Here, I plot the density of the left/right dimension for each group:

lrdist_pundits_logo.png

This plot shows that the centrist pundit class is more or less distributed around zero on this left/right ideological scale, which makes sense. It also shows a single peaked distribution on the right, with a long tail toward the left representing a handful of conservative accounts who are widely seen as heterodox, particularly in the Trump era (David Frum and Ross Douthat are part of this long tail), as well as two clusters of generally liberal and farther left users, respectively, identified by the left snowball sample.

Next, we plot the same distributions on the establishment/outsider dimension. As one might expect, centrist pundits are much more establishment-oriented than those identified by the left and right snowball samples. However, this plot also shows that while left pundits are fairly evenly distributed across the establishment/outsider dimension, right pundits are more densely clustered near the outsider pole. This may reflect a greater propensity on the right to get their news from alternate sources, a lower propensity to engage with ideological opponents, both, or something else -- we’d need to conduct more analysis to say one way or the other.

eodist_pundits_logo.png

Limitations

There are a few ways that analyses like these could go wrong that I should mention here before I wrap up. First, snowball samples are a less subjective way of establishing the pundit universe than coming up with all of the accounts off the top of my head would have been, but it does require a subjective set of accounts in order to seed the snowball sample. I tried to come up with a representative range of seed accounts, but starting with a different set may have yielded a different universe of pundits. Second, the number of accounts returned in the API requests for how many users each pundit follows is capped at 5,000 and some (particularly alt-right) accounts follow far more than 5,000 accounts -- likely due to them buying followers for publicity purposes. This could conceivably affect the ideological distributions, since we’re only able to measure overlap based on 5,000 of the accounts these users follow and, since they’re following accounts indiscriminately, their follows don’t convey as much information (Charlie Kirk, for example, did not make a conscious decision to hit the “follow” button 255 thousand times, and he is not placed well using this method). Finally, it is important to keep in mind that these dimensions are based on one specific type of behavior on Twitter -- whose tweets pundits choose to see -- and can obviously obscure other meaningful aspects of ideology that we may care about. Matt Yglesias and Elizabeth Bruenig, for example, land in a similar place on this two-dimensional measure of Twitter ideology despite having meaningful disagreements on matters of public policy.


Conclusion

Nobody is on Twitter, except for everyone who produces and discusses political content for a living. This group of political thought leaders spend their days working to define what it means to be on the left or right, or in the center. Here, I’ve laid out a systematic way of, first, defining who counts as a member of this chattering class and, second, how they arrange themselves in relation to one another on two key dimensions that we think about when we think about ideology.

Update: By popular demand, I’ve included the list of pundits and their scores below. If you have made it this far you have (hopefully) read the limitations section, which should caution you against using this list or these point estimates as ground truth. Please share responsibly.

Pundits in Two-Dimensional Space

accountsnowball_classdim1dim2
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