# Publishing patterns reflect
political polarization in news media

Nick Hagar  Communication Studies, Northwestern University  Johannes Wachs
Vienna University of Economics and Business  Complexity Science Hub Vienna
Emőke-Ágnes Horvát Direct correspondence to:
<EMAIL_ADDRESS>&<EMAIL_ADDRESS>This work
was partially supported by the U.S. National Science Foundation under Grant
No. IIS-1755873.  Communication Studies, Northwestern University

(January 2021)

###### Abstract

Digital news outlets rely on a variety of outside contributors, from freelance
journalists, to political commentators, to executives and politicians. These
external dependencies create a network among news outlets, traced along the
contributors they share. Using connections between outlets, we demonstrate how
contributors’ publishing trajectories tend to align with outlet political
leanings. We also show how polarized clustering of outlets translates to
differences in the topics of news covered and the style and tone of articles
published. In addition, we demonstrate how contributors who cross partisan
divides tend to focus on less explicitly political topics. This work addresses
an important gap in the media polarization literature, by highlighting how
structural factors on the production side of news media create an ecosystem
shaped by political leanings, independent of the priorities of any one person
or organization.

##  1 Introduction

Political polarization in digital news consumption is well established. While
the existence of self-reinforcing isolation that is strong enough to override
individual agency—so called “filter bubbles”—is hotly contested, there is
substantial evidence of patterns of media consumption aligned with political
identity in the U.S. (Bakshy et al., 2015; Macy et al., 2019; Shi et al.,
2017). Whether by an explicit recognition of political identity or the
implicit association of certain behaviors and traits with a politically-
aligned community, in-group identity drives members of opposing political
groups into silos (Coleman, 1988; Dvir-Gvirsman, 2017). However, this work
only explores consumption dynamics.

As a result, researchers have often sought to test the efficacy of cross-
cutting content and conversation in reducing polarization, which exposes
members of opposing groups to challenging viewpoints (Heatherly et al., 2017).
The idea of intentional exposure to conflicting views finds its way into
proposed interventions. Past work on “bursting” filter bubbles introduced
tools that allow users to monitor the political polarity of their news
consumption, for example, or to seek out the opposing views on important
issues (Flaxman et al., 2016; Resnick et al., 2013). In an effort to support
consumers, other work imagines alternative modes of suggesting media to users
via recommendation systems (Helberger, 2019). These interventions address
exposure to news, but not its production.

In news production, research often focuses on the individual priorities and
professional norms countervailing to polarization. In particular, researchers
pay close attention to the professional norm of objectivity within journalism
and how it gets enacted to minimize partisan influence (Schudson & Anderson,
2009; McChesney, 2003). This perspective focuses on the individual journalist
as the counterpoint to polarization in news media, as they carefully construct
coverage with an eye toward fairness, balance, and accuracy (Ryan, 2001). And
while this view acknowledges the role of news producers in polarized
environments, it also fails to address the structural forces shaping that
production.

Research that does take a structural approaches finds ample evidence of
polarization in the distribution of digital news. The earliest political blogs
demonstrated an overwhelming preference for linking to and engaging in
dialogue with like-minded outlets (Adamic & Glance, 2005). Similarly, work on
the dissemination of digital election coverage found an isolated, self-
reinforcing group of right-leaning outlets, one that largely disengaged itself
from the rest of the news ecosystem (Benkler et al., 2018). Third-party social
media platforms used for news distribution seem to largely amplify these
producer preferences (Bakshy et al., 2015; Benkler et al., 2018; Wihbey et
al., 2019). Therefore, even in a world where media consumers have the agency
to cross political boundaries and discover opposing views, the distribution of
that media operates in such a way that discourages cross-cutting exposure.
Polarization is an integrated problem.

This study attempts to extend that structural view into the production of
digital news itself, by examining the movement patterns of news contributors
from outlet to outlet. We treat these movements as a network, in which edges
form between outlets who share contributors. Using thousands of stories from
13 news outlets—with manually-validated information about the journalists,
freelance writers, and political actors who wrote them—we demonstrate clear
patterns in the trajectories contributors take when they write for multiple
outlets. We then show that contributor trajectories across outlets align with
the underlying political leanings of the outlets in our sample, more than any
other characteristic of the outlets or their audiences. Furthermore, we show
that those politically-aligned clusters differ in internal structure, with a
highly interconnected cluster of right-leaning outlets. Finally, we link the
political clustering present in this network to the content contributors
produce. We find that, not only do both the topics writers cover in each
cluster and the ways in which they present this content differ, but also that
contributors that move between clusters tend to be less explicitly political.
Taken together, these findings challenge the value of bridging content in
reducing the polarization of media consumers. Rather, they demonstrate the
need for a more nuanced examination of media producers and their underlying
motivations.

##  2 Background

###  2.1 Polarization in News Consumption

Digital news exists in a fragmented ecosystem. In contrast to mass media like
broadcast or print, news consumers split their attention among a wide variety
of sources (Webster, 2016). Audiences splinter into small groups as low-
barrier, low-cost news sources tailor to their specific interests and
identities (Taneja & Wu, 2018).

One prominent dimension of this fragmentation is political polarization. Past
work has argued for the existence of “filter bubbles” or “echo chambers”,
completely isolated spheres of media consumption. The evidence for these kinds
of all-encompassing divisions in news consumption is limited (Bruns, 2019).
However, political beliefs and preferences are a clear factor for consumers in
news source and story selection. People generally prefer to read news that
aligns with their political positions (Flaxman et al., 2016). Selective
exposure along partisan lines also shapes readers’ perceptions of news source
credibility, thereby impacting what news they decide to engage with (Tsfati et
al., 2014).

Given this focus on audience preferences and agency, polarization via
fragmentation is treated primarily as a consumption behavior in the digital
news ecosystem. The driving forces of polarization are often seen as news
consumer choices, in terms of the sources that they select and the
perspectives they prefer to see reflected in news coverage. In contrast, the
countervailing forces to polarization are often treated as the purview of news
producers, who adhere to professional standards of balance and objectivity.

###  2.2 Objectivity and Professionalism in News Production

Many news organizations and individual journalists go to great lengths to
avoid the appearance of any partisan position or influence. Historically, this
was not the case. Up through the 19th century, U.S. newspapers explicitly
identified themselves with political parties or ideologies, positions that
influenced their coverage (Schudson, 2001).

As journalists developed a clearer and more cohesive professional identity
throughout the 20th century, industry-wide norms developed that shifted news’
presentation (Schudson, 2001). In particular, journalists developed a strong
link between professionalism and objectivity (Schudson & Anderson, 2009). This
objectivity seeks to balance the perspectives and positions represented in
news coverage, rather than advancing any particular narrative (Ryan, 2001).

The practice of objectivity in journalism takes several forms at the
individual and institutional level. For journalists, objectivity is often tied
to professional independence—they value their ability to pursue stories
without outside ideological influence (Beam et al., 2009). Striving for
impartiality often homogenizes news, drawing coverage toward a political
center that relies mostly on official sources to construct narratives
(McChesney, 2003). Objectivity is often an ethical ideal, not a value
reflected in published news. This is particularly the case for dissenting or
marginalized perspectives, which often get drowned out by these centralized,
official narratives (McChesney, 2012). Even so, objectivity, or at least its
appearance, benefits news organizations as a whole. News outlets depend on
credibility with their audience for sustainability (Singer, 1997). Credibility
and objectivity, while distinct concepts, are closely related: Credibility
arises from an audience’s perception of a news outlet’s trustworthiness, and
objectivity is a key measure of the amount of fairness or bias contained
within coverage (Kelly, 2019). News organizations therefore implement policies
and practices to appear more objective. The New York Times offers a productive
case study. At the institutional level, it has marketed itself as an objective
source of factual information, with slogans such as “The truth is worth it”.
At the individual level, it prohibits journalists from expressing “partisan
opinions” or “political views” on social media (Times, 2017). These approaches
complement each other, enforcing objectivity in practice and in the eyes of
the audience.

For institutional news sources and individual journalists, then, there is a
strong incentive to uphold this notion of objectivity. News outlets require a
level of political impartiality from their contributors, to avoid any
appearance of bias. Journalists are encouraged to meet these standards by a
professional code of ethics. And together, these norms and practices are
marketed to audiences as safeguards for a credible, trustworthy source of
news. Objectivity centers professional news production as the site of media
de-polarization, by providing a common, reliable information source.

###  2.3 Structural Polarization in News Distribution

The work laid out so far tends to identify polarization as a news consumption
trait, while construing objectivity and professionalism as traits of news
production. This framing puts producers and consumers at odds, and it also
suggests that de-polarization efforts should be aimed at audiences rather than
creators. There has been much focus on “bursting” news readers’ filter
bubbles, by giving them indications of the partisan slant of their consumption
habits or intentionally exposing them to opposing viewpoints (Resnick et al.,
2013; Flaxman et al., 2016). This approach attempts to tackle partisan
isolation in the digital news ecosystem by focusing on cross-cutting exposure
in consumption.

However, recent work has also examined the ways in which news production and
distribution—particularly in the U.S.—becomes politically polarized. One
driving aspect is commercial. Audiences engage most with perspectives that
align with their own (Flaxman et al., 2016). In response, news outlets may
gradually move further away from the center of the political spectrum, in an
effort to better capture certain subsets of news readers (Munger, 2020;
Anderson & McLaren, 2012). Algorithmic intermediaries also reinforce
polarization via distribution, in that they reward more extreme offerings via
recommendation (Ribeiro et al., 2020; Blex & Yasseri, 2020).

Even beyond these mechanisms, researchers have identified structural
characteristics of the digital news ecosystem that reinforce polarization. In
particular, Benkler et al. (2018) examine the pathways along which news
stories travel among outlets. They find that, across several modes of
distribution, right-leaning outlets tend to amplify each others’ stories most
often. They are also isolated from the rest of the media ecosystem, lacking
ties, in terms of hyperlinks and social media sharing, to center- or left-
leaning outlets. Outlets also diverge in the topics they cover, depending on
their political leaning. In this way, Benkler et al. (2018) paint a picture of
a media ecosystem that is polarized, not because of any individual agent or
behavior, but because of a broad structure of distribution. This account runs
counter to the idea of a professionalized, objective news media. It portrays
an integrated system of polarization, one that cannot be addressed by focusing
solely on the consumption patterns of news readers. It fundamentally addresses
the portability of news stories and perspectives among outlets and audiences,
and how that is affected by polarization.

Our work takes a similar structural view of news production. Rather than
examining the portability of news coverage, though, we are concerned with how
polarization affects the movement patterns of news producers themselves. Given
the strong individual focus on objectivity within the journalism profession,
we are interested in whether the movement of individual writers among outlets
is subject to the same structural pressures. This study addresses the
following questions:

RQ1: What patterns do news contributors follow when moving among outlets?

RQ2: Do the topics addressed by contributors vary, depending on an outlet’s
political leaning?

RQ3: Do contributors who cross political boundaries cover different topics
from those who do not?

##  3 Data

We started with a publicly-available data set of news articles scraped from
the homepages of major publishers (Thompson, 2017). The full data set contains
131,860 articles, published between June 2014 and July 2017, across 14
outlets: the New York Times, Breitbart, CNN, Business Insider, the Atlantic,
Fox News, Talking Points Memo, Buzzfeed News, National Review, New York Post,
the Guardian, NPR, Vox, and the Washington Post. We filtered this full data
set in three ways. First, we removed any records with author fields that do
not correspond to a person’s name. To accomplish this, we removed any stories
with bylines that matched generic phrases (e.g., “the editors”, “anonymous”),
outlets (e.g., “The Associated Press”, “NPR staff”), or that contained bylines
with multiple names. Second, because we are interested in movement between
outlets, we limited our sample to articles by contributors who appeared in at
least two outlets in the data set. Because Business Insider does not share any
contributors with other outlets, we removed it from our analysis. Finally, we
manually verified the remaining bylines, disambiguating cases of multiple
people sharing the same name. This process identifies 368 contributors, who
wrote 6,032 articles and are the focus of our analyses.

We next manually coded each contributor depending on their professional
background. First, there are three types of professional journalists in our
sample: Freelancers who write for multiple news outlets (97 names),
journalists who are full-time employees at an outlet but write for at least
one other (67 names), and journalists who move from one full-time job to
another over the course of our sample (43 names). Overall, then, 207 of the
contributors in our sample are professional journalists of some kind. We also
find a couple groups who write pieces in support of particular issues or
causes. This includes activists and think tank members (62 names) as well as
political commentators and politicians (27 names). Finally, there are smaller
groups of academics (33 names) and authors (33 names) who write pieces
primarily to promote their work. An additional 6 contributors do not fit into
these categories.

Within our sample of 368 identified contributors, we analyzed publishing
patterns for each individual (Figure 2a). Figure 1 presents the volume of
stories, number of outlets published in, and number of days from first to last
story for each contributor. We find that many contributors write across a
couple outlets, and the majority publish fewer than 100 stories. However,
there is widespread variation in how long contributors are actively publishing
stories throughout our sample, indicating differences in their tempo of
activity.

(a)

(b)

(c)

Figure 1: Distributions of a) story counts per contributor, b) unique news
outlets that published each contributor, and c) the number of days from each
contributor’s first to last story in our data.

At the outlet level, we use a variety of characteristics to evaluate our
eventual network’s structure (Table 1). These characteristics fall under three
broad types of measures, and they broadly capture information about the outlet
and the size and composition of its audience. First, we used two measures of
outlet size—total unique users and total time users spent reading each
outlet—as these are often utilized in studies attempting to classify news
outlets (e.g., Hindman, 2018; McCombs & Winter, 1981). Second, we used a
variety of audience characteristics—household size, whether or not a household
has children, the race and gender composition of the audience, where the
audience is located, and the median reader age and income. All measures except
income report audience size, in terms of unique users, for certain groups. For
example, household size reports total users for households with 1-2 and 3-5
members. All measures except region, age, and income report two groups, so we
calculated the ratio of one group to the other to reduce the number of
classifications. For audience region, we retained the largest region by number
of users. Audiences often identify with the news they read, particularly in
alternative media, which could create observable clusters of outlets within
shared communities (Benkler et al., 2018; Kaiser et al., 2019; Chiricos et
al., 1997; Couldry & Curran, 2003). Third we utilized a couple outlet
characteristics: traditional publishing medium and political leaning.
Publishing medium is a common classification approach for comparative work
(e.g., (Maier, 2010; Boczkowski & de Santos, 2007)), and while the articles we
analyze are all published online, outlets still maintain other dominant
distribution media that may inform their overall publishing strategy. We
utilized the outlet political leanings generated by (Bakshy et al., 2015) in
examining polarization on Facebook.

ComScore provides all our audience measures. For these, we obtained the mean
monthly value of each measure from January 2016 to December 2017, when most
articles were collected. We then divided each continuous measure into three
groups, each of the size $(max-min)/3$. For political leaning, we use a
measure devised by Bakshy et al. (2015). They analyze the self-reported
political affiliation of Facebook users, assigning news articles an average
alignment score based on who shared each article. They then average those
article-level scores at the website level, producing a site-level alignment
score ranging from -1 (left-leaning sharers) to 1 (right-leaning sharers).
Bakshy et al. (2015) coarsen this measure by quintiles. We follow a similar
approach, treating sites that fall outside of their central quintile as left-
or right-leaning.

Measure | Source
---|---
Total unique users | ComScore
Total time read | ComScore
Median reader income | ComScore
Median reader age | ComScore
Audience household size | ComScore
Audience children (yes/no ratio) | ComScore
Audience race | ComScore
Audience gender (M/F ratio) | ComScore
Audience region | ComScore
Outlet medium | Manual coding
Political leanings | See: (Bakshy et al., 2015)
Table 1: Measures used for classifying outlets, along with their sources.

##  4 Methods

Here we describe the analytic approach we used to examine the network between
outlets that maps news contributor movement, as well as to investigate the
topic and tone of stories published.

###  4.1 Contributor Network Structure

To evaluate the network structure of contributor movement, we constructed a
bipartite network of writers and outlets, utilizing only our filtered set of
writers. A writer and a outlet shared an edge if at least one of the writer’s
stories appeared in the outlet within our sample. From this network, we
constructed a one-mode projection for outlets, in which outlets share an edge
if at least one writer published an article in both during the considered time
frame (Figure 2b). Edges were weighted according to the number of writers
outlets share. The number of articles per contributor across outlets also
varies; however, it does not vary evenly, and the article count is less
relevant to our research question than the number of contributors shared. 31
pairs of outlets did not share any contributors.

We then compared this weighted projection to randomized bipartite networks of
news outlets and contributors, to check for significant connections between
outlets. We used a standard Markov Chain Monte Carlo algorithm to build random
networks from the observed bipartite network (Cobb & Chen, 2003; Tumminello et
al., 2011; Newman, 2001; Rao et al., 1996; Zweig & Kaufmann, 2011). We
attempted $m*log_{e}(m)$ edge switches, where $m$ is the number of edges in
the bipartite network (i.e., 784) to obtain one instance of a random network
that has a substantially different structure than the observed network (Gionis
et al., 2007). We generated a set of 10,000 random networks. Our Supplementary
Materials Table 1 show the significance of edges in the projection; we retain
edges where $Z>1.96$. This procedure generates distinct clusters of outlets.

###  4.2 Classification Evaluation

To evaluate the uncovered network clustering, we compared it to
classifications from the mixture of audience and outlet characteristics
described in the Data section (Table 1). We treated each of these
classifications as though they were partitions within our observed network,
then used that partition to calculate the modularity of the classification.
Ranging from -1 to 1, modularity describes the extent to which a network is
split into distinct clusters (Newman, 2010, 2006). By mapping classifications
onto our observed network as though they were clusters, essentially we
calculated the extent to which each one aligned with the observed structure.

###  4.3 Topic Modeling

To analyze how the network structure of the journalism marketplace relates to
content, we employed topic models (Mohr & Bogdanov, 2013). Specifically, we
fit a topic model to the articles in our data set with Latent Dirichlet
Allocation (LDA) (Blei et al., 2002) using the Mallet program (and its default
parameter settings) (McCallum, 2002), accessed via the gensim library (Řehůřek
& Sojka, 2010) of the Python programming language. The result assigned each
document a vector of topics, allowing that a document consists of a
combination of topics and that words appear in multiple topics. The number of
topics is a parameter of the LDA algorithm–in the paper we presented results
from a model set to find twenty topics. We found similar results, namely
significant differences in topic prevalence across the clusters of outlets
when we fit the model to 15, 25, and 30 topics.

Before we fit the topic model, we processed the text of the articles. We
extracted each word or token, lower-casing all characters and removing
apostrophes. We removed English-language stop words and stemmed the remaining
words using the Porter stemmer. We removed words that occurred in more than
one-third of articles and those that occurred in less than 10 articles in our
corpus. The general goal of these steps was to reduce the noise of the data
while keeping its main signals relevant to our research intact (Hopkins &
King, 2010). The order of our processing pipeline is in line with recent
recommendations of best practices in topic modeling for communication research
(Maier et al., 2018).

Our network analysis finds a clear two-cluster structure, one that maps onto
outlets’ political leanings. We checked whether there are significant
differences in content between our derived outlet clusters by comparing the
topic vectors of their articles. We identified which topics are over-
represented among articles from individual clusters by calculating the average
topic vector of articles in the cluster and then comparing this vector to
average topic vectors calculated after randomizing the political lean of each
article’s outlet. This null model, which we generate 1,000 times, presents a
random assignment of political label. For each topic, we calculated a Z-score
comparing the prevalence of the topic in the observed articles with the
average prevalence of the topic after randomization and scale by the standard
deviation. We conducted this process separately for articles in each cluster.

###  4.4 LIWC Features

Besides the significant differences in content between the derived clusters,
we also examine important stylistic differences. This inquiry is driven by the
observed effect of news story presentation on audience reception, especially
along partisan lines. In particular, past work has demonstrated how stories
that are subjective and convey emotion reinforce partisan isolation in news
consumption and increase virality on social media (Flaxman et al., 2016;
Berger & Milkman, 2012; Xu et al., 2020). Here we investigate how these
semantic aspects of news stories vary between our outlet clusters. We apply
the Linguistic Inquiry and Word Count (LIWC) dictionary (Tausczik &
Pennebaker, 2010), a widely used tool for investigating stylistic properties
of text, to each article in our corpus. LIWC assigns text scores in various
linguistic (e.g., the use of pronouns, prepositions, punctuation) and
psychological (e.g., the use of words with significant positive or negative
emotional valence) dimensions curated by human experts. LIWC features have
been used to analyze the effectiveness of various kinds of persuasive writing
from crowdfunding pitches (Horvát et al., 2018) to public advocacy appeals
(Bail et al., 2017). A study of democrats and republicans on Twitter using
LIWC found significant differences between the characteristic linguistic style
of the two groups (Sylwester & Purver, 2015), for instance that republicans
were more likely to use words expressing negative emotion.

Using the LIWC software, we scored each article in our corpus along 83
dimensions of linguistic features. We also considered two additional
sentiment-based features from the VADER library (Hutto & Gilbert, 2014). We
compared the distribution of each feature between the politically-aligned
clusters using a Mann-Whitney U test. We find that 50 out of 85 features have
significant differences (Bonferroni corrected $p<.01$) in their distributions
between the two groups of articles. The number of significant differences
suggests substantial stylistic differences in the writing presented in the two
clusters. The full table of features and differences are presented in
Supplementary Materials Section 2.

##  5 Results

To address RQ1, we first investigated the patterns common across contributors’
movements. Since movement occurs at the level of the news outlet, we analyzed
what contributors’ next steps tended to be from any given outlet (e.g., do
contributors who write a story for the New York Times tend to also write for
the Washington Post?). Fig. 2c demonstrates these trends, by showing our
outlet-to-outlet network with significant edges only. This network of
statistically significant movements between news outlets reveals striking
patterns in contributors’ publishing histories. In particular, two clusters of
news outlets emerge: One low-density cluster comprised of nine outlets, and a
dense cluster of four outlets. In the low-density cluster, only three outlets
have connections with more than two outlets, creating a chain-like structure.
From a contributor perspective, this indicates that jumps around the cluster
are unlikely, but that travel to each outlet from every other is possible. In
the high-density cluster, however, 5 of the 6 possible edges are present,
indicating high mobility throughout the cluster. Between clusters, though,
movement is highly unlikely. Outlets across clusters do not just lack
significant positive connections; they have statistically significant negative
edges. See the Supplementary Materials Table 1 for further details.

Figure 2: Mapping contributors’ trajectories between news outlets. a) Example
trajectories indicating the outlets for which the selected two contributors
wrote articles. b) Illustration of how we build the network of outlets based
on which pairs of outlets shared contributors. c) Significant connections
between news outlets ($Z>1.96$), colored according to political leaning scores
assigned by (Bakshy et al., 2015). d) Modularity for each outlet
classification applied to the outlet network.

This clustering structure suggests that there are some constraints or barriers
to the movement of contributors between outlets, in particular as contributors
seem to lack the flexibility to move between clusters. However, those
constraints apply unevenly. Looking across contributor types, the majority of
non-journalist authors, academics, and politicians cross between clusters at
least once. In addition, politicians make up 36% of all cross-cluster
contributors, but only 5% of single-cluster contributors. It seems that some
kinds of news, particularly those associated with politics, travel easier than
others. However, less than a third of freelancers and other types of
journalists move between clusters. For them, association with one cluster may
represent a choice between two mutually-exclusive publishing paths, which have
difficult-to-reverse consequences for future opportunities. Furthermore, these
patterns are observable without taking into account any journalist or outlet
attributes. Rather, they are structural characteristics that emerge from a
combination of the editorial process and labor market. If the market structure
of contributor-outlet relationships is built out of implicit patterns in
activity, what heuristics—audience size or composition, for example—align with
this structure? Are new classification mechanisms needed to understand the
outlet landscape?

###  5.1 Structure of Contributors’ Movements between News Outlets

Traditionally, work that examines multiple outlets has divided them by their
publishing medium—comparing print newspapers to news websites, for example, or
radio to television (Maier, 2010). In this network, though, the outlet’s
traditional medium does not seem to align with the clusters we observe:
magazine contributors also write pieces for radio and digital-native sites,
for example journalists shared by _The Atlantic_ , _NPR_ , and _Vox_. To
validate this observation, we categorized each outlet using a variety of
metadata, described in the Data section. Then, we measured how well each of
these classifications captures the clustering dynamic in our observed data
using modularity (see details in Methods section). Our goal is to determine if
traditionally-accepted classification strategies can capture similar
information about contributor movement patterns as our network structure, and
if not, whether any other characteristics might provide a useful proxy.

In Figure 2d, we show the modularity of each classification. Accordingly,
outlet political leanings assigned by (Bakshy et al., 2015) correspond best to
the observed clusterings. If we map the leanings of each outlet to our
observed network (Figure 2c), we see a dense cluster of right-leaning outlets
and a loose grouping of left- and center-leaning ones. Given this dichotomy,
we subsequently refer to these clusters as left/center-leaning and right-
leaning. This finding suggests that the political leaning of a news outlet is
a key structural factor in where contributors will publish, pointing, as in
prior work, to a polarized online news ecosystem (Benkler et al., 2018; Bakshy
et al., 2015; Kaiser et al., 2019; Wihbey et al., 2019; Adamic & Glance,
2005). While this structure impacts movement, though, does it affect the
content of news articles across the network?

###  5.2 News Coverage Effects

The Z-scores derived from our topic model comparison across clusters (see
Methods) indicate the most relevant words of topics statistically over-
represented among left/center- and right-leaning outlets in the first columns
of Table 5.2. A Z-score with absolute value greater than 1.96, corresponding
to a p-value of 0.05, is considered a statistically significant deviation from
the null hypothesis that cluster lean labels are unrelated to the distribution
of topics. Articles appearing in center/left-leaning outlets are significantly
more likely to be about science and research (Topic 8), the media and press
(Topic 3), and healthcare (Topic 16), among others. In right-leaning outlets,
articles are more likely to be about conservatism and liberalism (Topic 10),
prominent Democratic Party politicians (Topic 13), and Republican politics
(Topic 4).

17 out of 20 topics are significantly over-represented among writings
appearing in either the right or left-leaning clusters of outlets. In relation
to RQ2, this indicates that the partition of outlets according to transitions
by contributors aligns with differences in content. Our findings prompt a
question: Do those contributors that do transition between the left and right-
leaning clusters adapt their writing topics to the venue? Or do these
transitioning contributors fill in particular niches within the partisan
spheres they visit?

To address this question, consider three groups of contributors: those who
write only for center/left-leaning outlets, those who write only for right-
leaning outlets, and those who write for both. Prior literature suggests that
these groups will focus on different topics, for two reasons. First, if
outlets gravitate toward certain political positions, they will often
emphasize pieces of information relevant and favorable to that political
group, and omit those that are not (Bernhardt et al., 2008). Therefore, if
contributors write stories that are in line with the editorial approach of the
outlet as a whole, we would also expect the information they produce to shift
depending on their group of focus.

Second, it is challenging for either individual journalists or outlets as
institutions to credibly maintain multiple political positions in their work
(Anderson & McLaren, 2012). A contributor who writes a piece about the dangers
of climate change, for example, cannot also credibly write a piece denying its
existence. It is then in contributors’ best interest to establish and develop
a consistent perspective in their writing throughout their careers. The
contributors who remain in only one cluster will generate that cluster’s
representative content (e.g., freelancers publishing in left-leaning outlets
will focus on the media), while contributors who move between clusters will
write about topics that aren’t particular to either. In other words, in
response to RQ3, we hypothesize that contributors moving between the left and
right-leaning outlets tend to write about politically more neutral topics.

lrrrr Topic: Keywords CntrLeft Z Right Z Trans-CntrLeft Z Trans-Right Z

1: attack, isi*, islam, muslim, war 0.42 0.8 -3.69 -5.29
2: vote, voter, percent, parti*, poll -2.63 4.07 0.52 0.58
3: news, media, post, press, twitter 9.99 -9.97 -5.17 -1.16
4: senat*, cruz, candid*, gop, parti* -5.62 7.24 2.20 4.70
5: realli*,talk, didn*, lot, someth* 4.75 -0.86 -5.55 0.38
6: game, play, team, season, serv* 6.31 -5.65 1.86 6.85
7: women, school, children, famili*, student -1.04 3.16 -4.33 5.62
8: research, studi*, human, found, univers* 13.07 -12.37 -7.51 2.18
9: film, music, play, movi*, book 5.23 -3.47-2.66 3.37
10: conserv*, power, left, liber*,fact -23.59 27.43 11.93 -4.57
11: compani*, million, bank, busi*, billion -6.64 -8.7 -3.2413.07
12: court, law, immigr*, case, rule -0.36 -0.76 18.86 -1.25
13: clinton, obama, hillari*, bill, email -15.61 14.45 2.3 -6.63
14: white, black, christian, commun*, religi* -3.3 5.38 -1.29 -2.86
15: tax, percent, job, econom*, rate -3.92 3.16 -0.28 -0.91
16: health, care, plan, bill, insur* 7.7 -7.14 -3.64 -0.02
17: china, obama, unit, iran, foreign 3.11 -2.89 6.92 1.35
18: offici*, investig*, secur*, inform, depart 6.2 -6.21 -2.68 -5.58
19: citi*, build, water, climat*,area 4.69 -5.1 -1.54 -0.65
20: polic*, offic*, crime, gun, case -2.06 2.03 -0.50 -4.51

Table 2: Topic keywords and their statistical over-representation (bold) and
under-representation (italic) within news outlet clusters. The first two
columns of Z-scores present statistical over/under-representation of topics
within the center/left- and right-leaning clusters. The last two columns
record the statistical over/under-representations of topics in articles by
contributors who move between clusters, compared to those by contributors who
stay within the center/left- and right-leaning clusters, respectively.

We tested this idea by considering within-cluster differences. For both the
left and right-leaning cluster of outlets we compared two kinds of
contributors: those who stayed within the cluster, which we call purists, and
those who transitioned. For example, among all articles in right-leaning
outlets we compare which topics tend to be covered by purists and those which
tend to be covered by transitioning contributors. We repeated a similar
experiment to the one used to determine topics over-represented among left and
right-leaning outlets. Within a cluster we calculated the average topic vector
of articles written by purists. We compared the average topic vector to those
calculated from 1,000 randomizations realized by shuffling the
purist/transitioning contributor labels. This randomization allowed us to test
the statistical over-representation of topics by purists or transitioning
contributors within the partisan clusters.

The results of this investigation are shown in the last two columns of Table
5.2 for articles in left-leaning outlets and right-leaning outlets,
respectively. Here we report the relative prevalence of topics in articles
written by transitioning authors within the two clusters, compared to their
purist counterparts. In both clusters there are many topics which are
significantly over and under-represented in the writings of transitioning
authors. This indicates that switching contributors in both partisan clusters
occupy specific niches with their writing.

Among the articles in right-leaning outlets, transitioning contributors are
more likely to write about finance (Topic 11), sports (Topic 6), and families
(Topic 7), among other topics. They are significantly less likely to write
about Democratic Party politicians (Topic 13), investigations (Topic 18), and
conflict in the Middle East (Topic 1). We note that of the six topics
significantly under-represented in the writings of transitioning contributors
relative to purist, three topics are globally over-represented in right-
leaning outlets (Topics 10, 13, 14). We interpret this as suggesting that
transitioning contributors are less likely to write about extremely partisan
topics.

The same analysis of left-leaning outlets suggests a similar pattern.
Transitioning contributors writing for left-leaning outlets seem to avoid
topics over-represented in those outlets on a global level (Topics 3, 8, 18).
Two of the five topics in which switching contributors are over-represented
are globally over-represented among right-leaning outlets. Future work should
investigate whether this observed relationship is because transitioning
contributors avoid partisan topics in order to remain widely employable, or
perhaps because the kind of contributor who is able to switch between the
partisan clusters tends to specialize in these topics.

###  5.3 Stylistic Differences in Language

Our LIWC and sentiment analysis also generate clear differences across outlet
clusters. We report the top six most distinguishing features by effect size
(measured by the AUC) in Table 5.3; the full table of features can be found in
our Supplementary Materials, Section 2. Three of these features are
significantly over-represented in articles in center/left-leaning outlets:
hear, capturing the use of words describing the act of listening; percept,
capturing the use of observational words relating to perception; and
focuspast, capturing the use of past-tense verbs. Three are over-represented
in right-leaning outlets: affect, counting the use of words with significant
psychological content; negemo, counting the frequency of words with negative
emotional connotation; and certain, counting the use absolute words such as
“always” or “never”.

Feature M-W U AUC CntrLeft Avg. Right Avg. Diff. Bonf. P
The top six LIWC features distinguishing texts from center/left- (bold) and
right-leaning (italic) outlets, by their AUC scores. 0.85lccccccc hear &
3144691.0 0.65 1.08 0.71 0.37 $<10^{-89}$
affect 3242798.0 0.64 4.33 5.10 -0.77 $<10^{-77}$
percept 3260857.5 0.64 2.38 1.87 0.51 $<10^{-75}$
negemo 3438952.5 0.62 1.88 2.36 -0.48 $<10^{-55}$
certain 3476109.5 0.62 1.06 1.30 -0.24 $<10^{-51}$
focuspast 3616885.0 0.60 4.09 3.45 0.64 $<10^{-38}$

At a high level, then, we observe widespread differences in the relative usage
of certain linguistic and semantic markers across right- and center/left-
leaning outlets, giving us an affirmative answer to RQ2. Not only are
audiences across these clusters exposed to different content; that content is
also subject to differing presentations. In particular, we see that the usage
of affect and emotion varies across these clusters, suggesting that the
subjective frames with which writers present news stories depend on the
preferences of the outlet and its audience.

##  6 Discussion

This work addresses an understudied area of news media polarization:
structural production forces driving partisan leanings. By constructing a
cross-outlet network purely based on contributor movement patterns, we show a
clear partisan divide within the digital news ecosystem. That divide appears
without any explicit consideration of audience behavior or preferences.

Our results demonstrate the ways in which structural factors can work against
individual ones, such as a professional commitment to objectivity. Some
contributors in our sample are not journalists, and some are explicitly
political figures. However, those that do fall under the umbrella of
professional journalism often stay within partisan bounds. Somewhere within
the editorial process of pitching, selecting contributors, assigning stories,
and producing news coverage, a dynamic arises that structurally prefers
contributors whose publishing histories ideologically align with a
publication’s own. This may arise from institutional policies, from individual
editors’ preferences, or from the pitching process of individual contributors
(Christin & Petre, 2020; Rosenkranz, 2018). In fact, the driving mechanisms
may differ between outlets or individuals. These possible mechanisms warrant
further study. Given the ways in which objectivity is so forcefully conveyed
as a key standard at many outlets, it is also worth examining where outlet
interactions with structural forces causes this norm to break down.

Polarized contributor isolation also shows up in the topic and style of news
coverage they produce, wherein distinct areas of interest and modes of
presentation arise for each outlet cluster. The polarization effect might
originate with contributor movement, but it also affects news coverage
production at the story level. Prior work has demonstrated differences in news
coverage approaches across the political spectrum (Xu et al., 2020; Schiffer,
2006). But the key here is that those differences don’t exist in isolation,
but are rather part of a larger media system that reinforces itself to drive
polarization. Because of the integrated nature of polarization within this
system, we cannot say what “causes” it. We can point to specific locations or
mechanisms by which it appears, such as selective exposure or profit
maximization at the outlet level (Stroud, 2010; Anderson & McLaren, 2012).
However, saying any of those things _cause_ polarization, or are even the main
site of polarization, runs the risk of leading to pinpoint interventions that
are broadly ineffectual. Audience-targeted interventions ignore producers’
incentives to publish traffic-driving partisan coverage. Producer-targeted
interventions ignore audiences’ desires for similar perspectives to their own.
Neither fully grapples with the feedback mechanism—running through the
production, distribution, and consumption of digital news–tied to revenue
incentives outlets must acknowledge to remain sustainable (Anderson & McLaren,
2012; McChesney, 2012). Future work should aim to work toward holistic
interventions that address this integrated polarization.

We also identify topics less related to politics that are more common for
contributors who move between clusters, a potential area for exploration in
future work. In particular, it would be worthwhile to investigate whether the
commonality of some topics also holds from an audience perspective–does sports
coverage cut across political preferences, or does the partisan affiliation of
a particular outlet affect all the coverage it produces?

Finally, in addition to addressing the broader area of news polarization
research, this study also engages directly with structural production
polarization work. Benkler et al. (2018) identify a very similar network
structure in news media distribution to what we see here: a loosely connected
group of left/center-leaning outlets, a dense core of right-leaning outlets,
and very little activity between. Using a totally separate sample, and
examining a different aspect of news production, we find an ecosystem with
identical characteristics. Benkler et al. (2018) also demonstrate how the
periphery of right-leaning outlets amplify conspiracy theories and
misinformation into the core of mainstream media. In contrast, we see very
little movement from this periphery outward, as contributors are significantly
unlikely to move between clusters. This network structure is potentially good
for stemming the flow of misinformation, but potentially bad for amplifying
the content-based partisan leanings we see in both clusters.

###  6.1 Limitations

This study has several important limitations. First, we cannot control for all
the potential biases that may have arisen in data collection via scraping. We
demonstrate the robustness of our results to differences in sample and
significance level with additional checks. First, we check the robustness of
the network to varying Z-score thresholds (Supplementary Materials, Section
1.1). We also examine the impact of removing individual outlets from the
network on its structure (Supplementary Materials, Section 1.2). However, it
is still important to acknowledge potential biases introduced by this dataset.

Similarly, our sample is only a subset of all news outlets. While the outlets
used here are prominent sources of digital media from across the political
spectrum, they are only one part of the news ecosystem. Future work should
attempt to broaden the outlets used.

Finally, outlet political leaning is a difficult quality to measure. It
originates from the perspective of the audience, rather than any objective
measure of the news coverage an outlet produces. We use a rigorous, peer-
reviewed method to ascertain our scores (Bakshy et al., 2015), but other
approaches may categorize outlets differently.

###  6.2 Conclusions

This study demonstrates the polarized structure of digital news contributor
movement. It ties that structure to differences in news coverage content and
perspective across the political spectrum, and it highlights the topic
differential across inter- and intra-cluster movement. Together, these results
show the interconnected nature of polarization and its consequences across
various aspects of news production. These findings fill an important gap in
the literature on news media, highlighting structural factors on the
production side which may contribute to polarization.

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##  7 Supplementary Materials

###  7.1 Network robustness

####  7.1.1 Z-score threshold

We recognize that there are potential sensitivities in our analysis to the
Z-score threshold we have chosen. In particular, it is possible that edges
were barely excluded at the $Z>1.96$ threshold that would change some of the
clustering dynamic we observe, or that edges crucial to the structure of the
clusters would disappear at a stricter threshold. To account for these
scenarios, we construct additional networks with edges selected by varying
Z-scores. In addition to the network we present in the main text, which
filters out edges based on a $Z>1.96$ threshold, we also check 1.64, 2.58, and
3.29. No additional analyses break down the clustering dynamic we observe in
our initial network. We present the underlying values for each network edge in
Table 7.1.1.

At $Z>1.64$, one edge gets added to the left-leaning cluster (between The
Guardian and Vox). Neither the clusters themselves or the underlying
structures–a dense right-leaning cluster versus a more chain-like left-leaning
cluster–are disturbed.

At $Z>2.58$, eight of the 24 significant edges in our initial projection are
lost. Four of these are negative edges across clusters, and three are in the
left-leaning cluster. This means that the structure of the left leaning
cluster becomes weaker, but, while the two clusters no longer have a
significant negative association, they are still not connected.

At $Z>3.29$, eight additional edges are lost. Once again, we see the weakening
of the left-leaning cluster, as well as the loss of negative edges between
clusters. Strikingly, the right-leaning cluster remains almost entirely
intact, even at this level.

Underlying frequencies of shared contributors between news outlets and
p-values/Z-scores generated by SICOP From To Shared journalists p Z-score
llrrr NationalReview NewYorkPost 43 0.00 9.26
NationalReview WashingtonPost 24 0.86 -0.18
NationalReview Atlantic 6 0.01 -2.50
NationalReview BuzzfeedNews 1 0.25 -1.14
NationalReview Breitbart 18 0.00 7.41
NationalReview FoxNews 13 0.00 3.52
NationalReview Guardian 2 0.00 -3.07
NationalReview Vox 1 0.00 -2.88
NewYorkPost NewYorkTimes 2 0.01 -2.57
NewYorkPost WashingtonPost 16 0.04 -2.08
NewYorkPost Atlantic 2 0.00 -3.60
NewYorkPost BuzzfeedNews 1 0.30 -1.03
NewYorkPost Breitbart 8 0.03 2.24
NewYorkPost FoxNews 22 0.00 8.23
NewYorkPost Guardian 4 0.01 -2.48
NewYorkPost NPR 1 0.00 -3.20
NewYorkTimes WashingtonPost 17 0.54 0.61
NewYorkTimes Atlantic 10 0.57 0.57
NewYorkTimes BuzzfeedNews 2 0.78 0.28
NewYorkTimes Guardian 16 0.00 4.15
NewYorkTimes NPR 13 0.00 2.89
NewYorkTimes Vox 4 0.56 -0.58
WashingtonPost Atlantic 28 0.38 0.88
WashingtonPost BuzzfeedNews 3 0.44 -0.78
WashingtonPost Breitbart 5 0.18 -1.33
WashingtonPost FoxNews 2 0.01 -2.79
WashingtonPost Guardian 17 0.97 -0.04
WashingtonPost NPR 20 0.37 0.89
WashingtonPost Vox 24 0.00 3.08
WashingtonPost CNN 8 0.01 2.45
WashingtonPost TalkingPointsMemo 1 0.34 0.95
Atlantic BuzzfeedNews 3 0.74 0.33
Atlantic Guardian 15 0.05 1.97
Atlantic NPR 21 0.00 4.16
Atlantic Vox 16 0.00 3.28
Atlantic CNN 3 0.35 0.94
BuzzfeedNews Guardian 4 0.10 1.64
BuzzfeedNews NPR 1 0.49 -0.69
BuzzfeedNews Vox 1 0.67 -0.43
BuzzfeedNews CNN 2 0.01 2.77
Breitbart FoxNews 6 0.00 3.67
Guardian NPR 8 0.81 0.24
Guardian Vox 10 0.06 1.90
NPR Vox 11 0.02 2.32
NPR CNN 1 0.73 -0.35
Vox CNN 1 0.93 -0.09
CNN TalkingPointsMemo 1 0.00 4.98

###  7.2 Outlets included

Because our data are collected secondhand, they are subject to a couple
potential biases that might affect our results. First, the selection of
outlets within the sample may shape the network structure. Second, unobserved
inconsistencies in data collection across outlets could distort the
contributor publishing histories we use to evaluate edge significance. To
better understand the potential impact of these factors, we removed one outlet
at a time from our sample, then ran SICOP again on each set of 12 remaining
outlets. Rather than just removing a node from our final network, this
procedure allows new edge weights to be calculated without the influence of a
particular outlet.

In no case did the overall network structure we observe drastically change as
a result of this procedure. We still see a loose collection of left-/center-
leaning outlets, and a dense cluster of right-leaning ones. In some cases, the
left/center cluster breaks into multiple clusters, or into one cluster with
isolates (e.g., Fig. 3 a). This follows naturally from the cluster’s observed
chain-like structure—removing central nodes causes the chain to break.
However, none of these iterations change the looseness of the overall
structure. Similarly, in the right-leaning cluster, removing any one of the
four nodes simply causes edges to form among the other three (e.g., Fig. 3b).
Most importantly, in no iteration do any significant edges form between
clusters. Thus, while the particular structure of the left/center cluster does
show some sensitivity to the outlets included, the overall division and the
characteristics of the clusters within this network remain consistent.

(a)

(b)

Figure 3: Outlet-outlet projections with a) NPR removed and b) the New York
Post removed. These cases are representative of the minor changes in network
structure created by dropping outlets from our sample, maintaining the
characteristics we highlight in our findings.

###  7.3 LIWC

Here we report the full list of LIWC (and VADER) features compared between the
two primary groups of articles. The entries are sorted by AUC.

Feature | M-W U | P-val. | AUC | CntrLeft Avg. | Right Avg. | Diff. | Bonf. P-val.
---|---|---|---|---|---|---|---
hear | 3144691.0 | 0.000 | 0.652 | 1.08 | 0.71 | 0.37 | 0.000
affect | 3242798.0 | 0.000 | 0.641 | 4.33 | 5.10 | -0.77 | 0.000
percept | 3260857.5 | 0.000 | 0.639 | 2.38 | 1.87 | 0.51 | 0.000
negemo | 3438952.5 | 0.000 | 0.619 | 1.88 | 2.36 | -0.48 | 0.000
certain | 3476109.5 | 0.000 | 0.615 | 1.06 | 1.30 | -0.24 | 0.000
focuspast | 3616885.0 | 0.000 | 0.600 | 4.09 | 3.45 | 0.64 | 0.000
anger | 3625115.0 | 0.000 | 0.599 | 0.69 | 0.91 | -0.22 | 0.000
relativ | 3630524.5 | 0.000 | 0.598 | 13.63 | 12.72 | 0.91 | 0.000
negate | 3656126.5 | 0.000 | 0.595 | 1.16 | 1.36 | -0.20 | 0.000
posemo | 3780913.0 | 0.000 | 0.582 | 2.37 | 2.67 | -0.30 | 0.000
QMark | 3797283.5 | 0.000 | 0.580 | 0.18 | 0.25 | -0.07 | 0.000
space | 3797893.0 | 0.000 | 0.580 | 7.28 | 6.82 | 0.46 | 0.000
sad | 3818021.5 | 0.000 | 0.577 | 0.29 | 0.37 | -0.08 | 0.000
discrep | 3839167.0 | 0.000 | 0.575 | 1.19 | 1.37 | -0.18 | 0.000
differ | 3877426.5 | 0.000 | 0.571 | 2.73 | 2.97 | -0.24 | 0.000
Comma | 3878505.0 | 0.000 | 0.571 | 5.73 | 5.39 | 0.34 | 0.000
risk | 3897427.0 | 0.000 | 0.569 | 0.71 | 0.82 | -0.11 | 0.000
prep | 3902945.5 | 0.000 | 0.568 | 14.57 | 14.18 | 0.39 | 0.000
conj | 3913954.5 | 0.000 | 0.567 | 5.31 | 5.54 | -0.23 | 0.000
vaderCompound | 3928249.5 | 0.000 | 0.565 | 0.25 | 0.11 | 0.14 | 0.000
power | 3936100.0 | 0.000 | 0.564 | 4.23 | 4.55 | -0.32 | 0.000
cogproc | 3950743.5 | 0.000 | 0.563 | 9.58 | 10.09 | -0.51 | 0.000
time | 3982288.5 | 0.000 | 0.559 | 4.69 | 4.38 | 0.31 | 0.000
motion | 3997062.0 | 0.000 | 0.558 | 1.74 | 1.60 | 0.14 | 0.000
auxverb | 4016272.5 | 0.000 | 0.555 | 6.77 | 7.05 | -0.28 | 0.000
see | 4063008.0 | 0.000 | 0.550 | 0.87 | 0.77 | 0.10 | 0.000
Quote | 4110413.5 | 0.000 | 0.545 | 2.48 | 2.28 | 0.20 | 0.000
drives | 4116632.5 | 0.000 | 0.544 | 8.46 | 8.79 | -0.33 | 0.000
focuspresent | 4132848.0 | 0.000 | 0.543 | 7.19 | 7.46 | -0.27 | 0.000
assent | 4145328.5 | 0.000 | 0.541 | 0.06 | 0.09 | -0.03 | 0.000
adj | 4152054.5 | 0.000 | 0.540 | 4.77 | 4.94 | -0.17 | 0.000
death | 4158694.5 | 0.000 | 0.540 | 0.24 | 0.27 | -0.03 | 0.000
social | 4164598.0 | 0.000 | 0.539 | 8.96 | 8.55 | 0.41 | 0.000
relig | 4164692.0 | 0.000 | 0.539 | 0.33 | 0.41 | -0.08 | 0.000
i | 4164564.0 | 0.000 | 0.539 | 0.71 | 0.53 | 0.18 | 0.000
tbPolarity | 4178439.5 | 0.000 | 0.538 | 0.09 | 0.08 | 0.01 | 0.000
reward | 4181480.0 | 0.000 | 0.537 | 1.00 | 1.07 | -0.07 | 0.000
work | 4188183.5 | 0.000 | 0.536 | 4.60 | 4.31 | 0.29 | 0.000
home | 4195092.5 | 0.000 | 0.536 | 0.40 | 0.33 | 0.07 | 0.000
Apostro | 4190130.0 | 0.000 | 0.536 | 2.22 | 2.31 | -0.09 | 0.000
SemiC | 4208843.5 | 0.000 | 0.534 | 0.03 | 0.02 | 0.01 | 0.000
they | 4219078.0 | 0.000 | 0.533 | 0.86 | 0.95 | -0.09 | 0.000
money | 4232751.5 | 0.000 | 0.532 | 1.12 | 1.23 | -0.11 | 0.001
quant | 4235906.0 | 0.000 | 0.531 | 2.16 | 2.24 | -0.08 | 0.001
adverb | 4241383.5 | 0.000 | 0.531 | 3.54 | 3.64 | -0.10 | 0.002
ingest | 4234582.0 | 0.000 | 0.531 | 0.26 | 0.19 | 0.07 | 0.000
netspeak | 4249819.0 | 0.000 | 0.530 | 0.21 | 0.18 | 0.03 | 0.001
interrog | 4244529.0 | 0.000 | 0.530 | 1.44 | 1.39 | 0.05 | 0.002
AllPunc | 4255325.5 | 0.000 | 0.529 | 17.26 | 16.84 | 0.42 | 0.004
OtherP | 4258541.5 | 0.000 | 0.529 | 0.28 | 0.30 | -0.02 | 0.002
leisure | 4274775.5 | 0.000 | 0.527 | 1.03 | 0.90 | 0.13 | 0.013
function | 4314285.5 | 0.001 | 0.523 | 46.19 | 46.21 | -0.02 | 0.110
tentat | 4330995.5 | 0.003 | 0.521 | 2.18 | 2.25 | -0.07 | 0.241
anx | 4336993.5 | 0.004 | 0.520 | 0.36 | 0.39 | -0.03 | 0.306
verb | 4352186.5 | 0.007 | 0.518 | 12.57 | 12.28 | 0.29 | 0.606
affiliation | 4354677.0 | 0.008 | 0.518 | 1.86 | 1.81 | 0.05 | 0.671
achieve | 4353813.5 | 0.008 | 0.518 | 1.65 | 1.70 | -0.05 | 0.648
bio | 4350804.0 | 0.007 | 0.518 | 1.29 | 1.04 | 0.25 | 0.571
feel | 4360879.0 | 0.010 | 0.517 | 0.32 | 0.29 | 0.03 | 0.835
article | 4366549.5 | 0.012 | 0.517 | 8.54 | 8.42 | 0.12 | 1.074
informal | 4368808.5 | 0.013 | 0.516 | 0.38 | 0.38 | 0.00 | 1.152
shehe | 4373985.5 | 0.017 | 0.516 | 1.89 | 1.77 | 0.12 | 1.420
we | 4393997.0 | 0.033 | 0.514 | 0.59 | 0.63 | -0.04 | 2.829
sexual | 4407971.5 | 0.024 | 0.512 | 0.11 | 0.13 | -0.02 | 2.072
Period | 4411906.5 | 0.058 | 0.512 | 5.36 | 5.35 | 0.01 | 5.019
number | 4406451.0 | 0.050 | 0.512 | 2.05 | 2.36 | -0.31 | 4.258
swear | 4411774.5 | 0.008 | 0.512 | 0.03 | 0.03 | 0.00 | 0.656
nonflu | 4405019.5 | 0.031 | 0.512 | 0.08 | 0.08 | 0.00 | 2.694
focusfuture | 4421046.5 | 0.076 | 0.511 | 1.00 | 1.01 | -0.01 | 6.530
cause | 4422613.0 | 0.079 | 0.511 | 1.60 | 1.62 | -0.02 | 6.820
Colon | 4426143.5 | 0.086 | 0.510 | 0.40 | 0.36 | 0.04 | 7.421
compare | 4457029.5 | 0.184 | 0.507 | 2.65 | 2.66 | -0.01 | 15.855
female | 4451589.0 | 0.156 | 0.507 | 0.69 | 0.63 | 0.06 | 13.447
health | 4456542.5 | 0.181 | 0.507 | 0.65 | 0.47 | 0.18 | 15.608
friend | 4467566.0 | 0.220 | 0.506 | 0.16 | 0.17 | -0.01 | 18.889
Parenth | 4464664.0 | 0.212 | 0.506 | 0.56 | 0.52 | 0.04 | 18.200
Exclam | 4460400.0 | 0.098 | 0.506 | 0.04 | 0.05 | -0.01 | 8.416
ipron | 4476870.0 | 0.273 | 0.505 | 4.74 | 4.71 | 0.03 | 23.461
male | 4498125.5 | 0.386 | 0.502 | 1.63 | 1.63 | 0.00 | 33.224
family | 4507666.5 | 0.438 | 0.501 | 0.27 | 0.29 | -0.02 | 37.705
you | 4510550.5 | 0.458 | 0.501 | 0.48 | 0.48 | 0.00 | 39.401
insight | 4508382.5 | 0.446 | 0.501 | 1.96 | 1.95 | 0.01 | 38.328
filler | 4509865.0 | 0.376 | 0.501 | 0.01 | 0.01 | 0.00 | 32.359
pronoun | 4507193.5 | 0.439 | 0.501 | 9.27 | 9.08 | 0.19 | 37.729
ppron | 4517453.5 | 0.499 | 0.500 | 4.53 | 4.37 | 0.16 | 42.933
body | 4515469.5 | 0.487 | 0.500 | 0.30 | 0.28 | 0.02 | 41.912


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