THIS IS A DRAFT OF A POST INTENDED FOR PUBLICATION BY REYNOLDS JOURNALISM INSTITUTE
Identifying the author behind New York Time’s op-ed from Inside the Trump White House, using data science
| BY: MICHAEL W. KEARNEY | 2018-09-06 |
Yesterday the New York Times published an opinion piece about the Resistance Inside the Trump White House. It was written by an anonymous source–identified only as “a senior official in the Trump administration.” It didn’t take long for President Trump to tweet his reactions:
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TREASON?
— Donald J. Trump (@realDonaldTrump) September 5, 2018
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Does the so-called “Senior Administration Official” really exist, or is it just the Failing New York Times with another phony source? If the GUTLESS anonymous person does indeed exist, the Times must, for National Security purposes, turn him/her over to government at once!
— Donald J. Trump (@realDonaldTrump) September 5, 2018
Early speculation about who authored the Times op-ed focused on the use of the word, “lodestar”, an usual term previously used by Vice President Mike Pence in a CBS interview. Others have argued that such an obvious use of a unique term is instead a form of intentional misdirection. With this back-and-forth about lodestar in mind, one can easily imagine how the exercise of identifying unique words and speculating about their re-occurrences could continue on, perhaps, indefinitely.
We may never know who actually authored the New York Times op-ed about the Resistance Inside the Trump Administration. In retrospect, we may even judge the search–to identify the anonymous author–itself as poor form, at least in the democratic sense. I’ll leave those judgments to the political scientists and democratic theorists. But, for now, I would like to point out that while the knee-jerk reaction to look at communication patterns among senior-level White House officials makes some sense, doing so without leveraging recent advances in data science would be a huge waste and, for me, a total bummer.
Ultimately, the question “can past communication records be used to help identify the author of the anonymous New York Times op-ed?” is an empirical one. It’s one that–thanks to the wonders of digital media and data science–we can start to gain insights from (whether accurate or not) in a relatively short amount of time. And since a lot of the work I do involves analyzing political communication on Twitter anyway, I figured I’d give it a shot as a way to demonstrate what’s possible with some data science training, plenty of data, and a little bit of time.
First, I grabbed the [reference] text from the actual New York Times op-ed.
- I collected op-ed text
Next, because I needed some writing/communication samples to compare against the text of the op-ed, I turned to Twitter–because it only seemed apropos given communication preferences of the current administration, and because I just so happened to maintain some open-source software for interacting with Twitter’s APIs. For the sake of time, I decided to limit my analysis to members of the President’s cabinet. Plus, given the only description we have of the author, it seemed fairly safe to assume that a senior official in the Trump administration would, at the very least, be an accurate description of a member of Trump’s Cabinet.
- I collected up to the most recent 3,200 tweets from each [Twitter-using] member of Trump’s cabinet
With a reference text and a number of samples from Twitter, I then split the op-ed text by paragraph to roughly match the length of tweets.
- I split op-ed text into paragraphs
Using this supply of texts matched to screen name or “op-ed” authors, I then extracted estimates for over 100 features (numeric representations of observed patterns in the text) for each string of text. Some examples of the features include capitalization, punctuation (commas, periods, exclamation points, etc.), use of white space, word length, sentence length, use of ‘to be’ verbs, and numerous thesaurus-like representations of word dimensions (similar to dividing commonly used words into eighty different topics and then measuring the extent to which each text used words from each topic).
- I converted each text into 107 numeric features
Finally, to get an actual measurement of similarity between the op-ed and the Twitter screen names, I averaged the numeric featured by author, and then used those values to estimate the correlations (measure of association ranging from -1 to 1) between the op-ed texts and the Twitter user texts.
- I estimated the correlation between the op-ed texts and the tweets posted by each Cabinet member account
You can find the code I used on Github. And here is the visual representation of the correlation coefficients:
This exercise is useful for illustrating how it’s possible to use data science to estimate the similarity between multiple texts, but it does not provide any conclusive evidence to answer the question of who authored the New York Times op-ed. In fact, there are numerous reasons why one should be skeptical of inferences made from this analysis. I will describe just a few of those limitations in the next few paragraphs.
First, the comparison texts are limited because they were designed for Twitter and not the New York Times. They were also authored by users who presumably were okay with their identity being connected to their Tweets (unlike the anonymous author behind the op-ed).
Second, the pool of comparison texts doesn’t even represent all of the possible options of people who would fit the description of a senior official in the Trump administration. For instance, this omits from consideration any Cabinet member who doesn’t have or use Twitter. It also omits anyone who works in the Trump administration but who is not an official member of the Cabinet.
Third, the text similarity analysis assumes the author of the anonymous op-ed didn’t try (or failed) to disguise their own communication patterns. Even if they did try to disguise themselves, it’s certainly possible that some communication patterns still slipped through the cracks, but it’s also possible they provided enough false positives (i.e., “lodestar”) that many algorithms would get it wrong.
Fourth, it also assumes the people who write the tweets are the actual people who they purport to represent. For example, we have reason to believe Trump does not operate the @POTUS account, but in most cases we never know for sure. It’s also entirely possible that some internal communications person influences the messages sent on behalf of several of those accounts. Or it’s possible that administrations tend to have overlapping communication patterns because they strive for monolithic communication and, as a consequence, there are a handful of near matches in any sample of texts composed by people working in the White House.
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