Tweets about WI Gubernatorial Race Part I: October 28 to Nov 6

Politically, Wisconsin is quite different from my home state of New York. It’s long been considered a purple, or swing, state. For that reason, Wisconsin has often received extra national attention when it comes to local or state-wide politics.

The 2018 Midterm Elections were another example of this, with many citizens around the country tracking Governor Scott Walker’s race against Superintendent Tony Evers. Today, I explore how Twitter talked about this race in the week leading up to Election night (October 28 to Nov 7). This post will focus on the lead-up to the election. Part II will focus on the last few hours of the election (12:30 to 2:30 on November 7, 2018).

(Note: Tweets were collected using the r package rtweets. All datetimes have been converted to CST. For more information about this collection and analysis, please scroll to the bottom)

A broad temporal view: Oct 28 to Nov 6

In the week leading up to the election, there were several noteworthy spikes. We focus on two in particular: November 1 (8-9pm) and November 4 (7pm).

November 1, 2018 from 8:00-9:59 pm

This was the largest spike for Walker in this week (1568 tweets in two hours). Far and away, the most common verb used was variants of “call” (e.g., “called”/”calls”/”calling”). This is because, that day, Governor Walker said that President Obama was "the biggest liar of the world.” This language (employed by non-journalists and journalists alike) was also employed in leads of news stories in Fox News and The Hill).

November 4, 2018 from 7:00-7:59 PM

Although this peak was not as prominent as the others explored here, it is one of the few times that Evers exceeded Walker in references on Twitter.

Many of these tweets appeared to be campaign-oriented tweets about Evers’ support for Wisconsin residents. Unlike the previous spike, there did not seem to be an event aligned with this moment in time. This suggests that this spike was campaign-induced, rather than naturally generated.

A closer look at Election Day

As can be seen in the above image, attention to the Walker/Evers election peaked after 12:00 AM CST, late in the night relative to other well-watched races that day. Votes rolled in minute by minute, with many outlets (including NYT, one of my main trackers) showing a less than 1% margin for several hours.

Methodology

Tweets were collected using Mike Kearney’s rtweets. I began my search at 2:40 AM CST on November 7, 2018, using the search terms “Scott Walker” OR “Tony Evers” OR “#wipolitics” OR “#wielection“. Twitter’s REST API provides an about 1% random sample of tweets. This yielded about 111,000 tweets.

Tweets were annotated for their part-of-speech and dependency using coreNLP. Within the corpus, there were over three million dependencies.

Time Series of IRA Activity on U.S. Social Media Platforms

So I've been toying around with some of the data on other social media platforms, now that much of it has been made publicly available. I'm looking forward to doing a more systematic analysis of the content. In the meantime, however, here are some counts of IRA activities on different social media platforms from 2015 to 2017. 

I was somewhat surprised to see that the time series did not line up as neatly as I thought they would have. Perhaps these strategies are meant to complement each other? This is where a deeper dive into the content or the account would be more useful. For example, perhaps conservative-imitating IRA accounts (e.g., Twitter's @TEN_GOP) responded to different things compared to liberal imitating IRA accounts (e.g., Facebook/Twitter's @Blacktivist group). 

Given the pending lockdown of information regarding this case, it is more important than ever to share and verify this information. It's a shame researcher do not get much access to this kind of data, as scientific rigor should be the minimum standard for analyzing potential foreign influences into American elections. 

Reddit Data Source: [Link]
Facebook Data Source: [Link]

Advertisements purchased by IRA on Facebook

Submissions to Reddit by IRA-controlled accounts

Tweets written by the IRA

Understanding a little more about recent coverage of Korean-U.S. relations through adjective use

Yesterday, U.S. President Trump pulled out of a "highly-anticipated" summit meeting with North Korea's Kim-Jung Un. Given the freshness of this story, it'll take some time collect enough articles to do an anlaysis of this specific incident. But, in the meantime, some interesting results from my analysis of Korean-U.S. relations in American news below.

(Data cleaned and analyzed using R tidytext, quanteda, and OpenNLP. Graphs produced by ggplot2 or MediaCloud.)

Count of articles using the words "Trump" and "North Korea" in top American news media (digital + traditional). Results gathered using MediaCloud archive.

Count of articles using the words "Trump" and "North Korea" in top American news media (digital + traditional). Results gathered using MediaCloud archive.

As we can see above, the majority of the coverage appeared to be between May 7 (when North Korea claimed to have demolished a nuclear test site) and May 21. Using those two weeks as my window, I pulled all articles referencing "Trump" and "North Korea" from four news outlets: CNN (n =96), Fox (n = 114), the New York Times (n = 89) and the Washington Post (208), a total of 507 news stories.

I tagged all the words in the news stories for their part of speech using OpenNLP. I then pulled out all the adjectives, removed duplicates, and screened them for accuracy (OpenNLP has an above 90% accuracy, but the human eye is critical to ensuring quality results). I finally looked at the use of these adjectives in relation to specific actors/parties (mainly North Korea, South Korea, and the United States). Given the effect of political personalization, I consider both the country name and the name of the leader (e.g., "North Korea" OR "Moon Jae-In" OR "President Moon" OR "Moon Jae In") as keywords. I retained the adjective if it appeared within three words of the NK, SK, or US keywords.

Raw counts are presented below (keep in mind the corpus is not perfectly balanced... also, sorry I was too lazy to reorder the charts XD Just so tired and wanted to practice some code):

Most commonly used adjectives related to Trump/U.S.

 

Most commonly used adjectives related to Kim Jung-Un and North Korea

 

Most commonly used adjectives related to President Moon and South Korea

How President Trump used modal verbs in his Syrian air strikes speech

A list of modal verb usage in President Trump's recent speech on the Syrian air strikes. "American" subjects are highlighted.

Modal (auxiliary) verbs (can, could, should, would, must, will, etc.) are an interesting subset of American language, as they are used to indicate "intention" (the way things "ought to" be or "should" be, or an evaluation of what "can" happen). Different modals have different degrees of "modal force."

Here, you'll see that the modal "can" is the most commonly used, often referring to non-American subjects (e.g., "nations of the world" or "our friends"). In the three instances "can" is used in relation to Americans, two are negations ("No amount of American blood..." and "we cannot..."). In the last instance, "can" is used to express a hope, rather than an intention.

By contrast, the other "can" modals are used effectively as threats and evaluations: "The nations of the world can be judged by the friends they keep" or "Increased engagement [...] can ensure that Iran does not profit..."

This distinction is important because it uses our allies and vague "international norms" to express how the world "can" be. Even though the U.S. is certainly an instigator, the modal verb usage implies that we are trying to distance ourselves from taking ownership of these air strikes. We frame this use of force as unavoidable, because of actions taken by other states. The singular use of "would" and "must" in relation to Russia reinforces this further, especially given that they are used so closely together (Russia was supposed to do something, and then they didn't, so they now must do something else).

To make a long story short, we express the necessity of use of force here by saying what we (the US) "cannot" do, and implying what "can" (implicitly "should") happen as a result.

This speech obviously deviates from President Trump's typical language use, but it goes to show the significance of "use of force": even rebels fall in line when they have to justify violence.

(Hoping to do some analysis of news around this air strike tonight as well!)

Interview with Cap Times

This past week, I interviewed with Capital Times in Madison to talk about a recent co-authored study about Russian propaganda in U.S. news media.

I'm glad that the writer, Lisa Speckhard, did a great job capturing my greatest concern with Russian influence and disinformation. We know that the Russians are not going to stop trying to infiltrate U.S. public discourse. They haven't stopped since WWII, and I doubt they ever will.

What we can do is ask ourselves (1) where are they likely to make their way into American political discourse and (2) what can we do [on our end] to stop it.

Journalists are in a special space, as gatekeepers of information, to both prevent and perpetuate Russian propaganda from amplifying. As we learned through this study, this gate is not impervious... especially now that there are so many gates.

In order to keep our public discourse "pure" (that is, not unknowingly manipulated by foreign influences), we need to be self-reflexive, vigilant, and careful. I am continually reminded of this when news organizations reach out to members of our team asking about various articles that have included IRA-linked tweets. We need more news organizations like this and like Slate, who continue to be critical of their journalistic routines. 

New UW Study on Russian Twitter Trolls in U.S. Media

This past week, my research team published a study on news media's use of tweets written by Russia's Internet Research Agency (a copy of the study can be found here).

We also wrote a parallel article with Columbia Journalism Review.

Importantly, we show that Russian tweets conveying stereotypical partisan beliefs were picked up by a variety of mainstream and partisan news outlets. We are particularly critical of news stories that use "strings of tweet" to represent the vox populi (voice of the people). Unlike the more traditional "man on the street" interviews, tweets used in news stories (particularly online ones) are difficult to verify.

However, as shown by the (admittedly shallow) penetration of IRA tweets, it is still important for journalists to verify these Twitter users to the best of their ability. Journalists can do so by corresponding outside of the tweet-o-sphere (e.g., email), trying to look up the user's name on a search engine, or by looking at that user's past social media history.

Digital, partisan news outlets were particularly susceptible to embedded these IRA tweets. Liberal and conservative organizations both used tweets to convey cheap talk (discourse that supports their position or criticizes their opponents'). If the goal of Russian disinformation in the United States was to increase doubt in the news media system and increase polarization in the civil sphere, the amplification of these messages through partisan outlets represent some measure of success.

 

Why does this matter? Aside from natural concerns about deceptive foreign practices to our public sphere, the appearance of these messages across a broad range of news organizations  (bost partisan and traditional, liberal and conservative) shows how little tweets were checked. It highlights a greater problem: our willingness to promote partisan messages to prove a political point, even if they have little to no journalistic value and are not verified.