# Shifting Polarization and Twitter News Influencers between two U.S.
Presidential Elections

James Flamino${}^{\text{1}}$, Alessandro Galezzi${}^{\text{2}}$, Stuart
Feldman${}^{\text{3}}$, Michael W. Macy${}^{\text{4}}$,
Brendan Cross${}^{\text{1}}$, Zhenkun Zhou${}^{\text{5}}$, Matteo
Serafino${}^{\text{6}}$, Alexandre Bovet${}^{\text{7}}$,
Hernán A. Makse${}^{\text{8}}$, Boleslaw K. Szymanski${}^{\text{1,9,*}}$
${}^{\text{1}}$Department of Computer Science and Network Science and
Technology Center,
Rensselaer Polytechnic Institute, Troy, NY, USA
${}^{\text{2}}$University of Brescia, Brescia, Italy
${}^{\text{3}}$Schmidt Futures, New York, NY, USA
${}^{\text{4}}$Departments of Information Science and Sociology, Cornell
University, Ithaca, NY, USA
${}^{\text{5}}$School of Statistics, Capital University of Economics and
Business, Beijing, China
${}^{\text{6}}$IMT School for Advanced Studies, 55100 Lucca, Italy
${}^{\text{7}}$Mathematical Institute, University of Oxford, UK
${}^{\text{8}}$Levich Institute and Physics Department, City College of New
York, New York, NY, USA
${}^{\text{9}}$Społeczna Akademia Nauk, Łódź, Poland
∗Corresponding author, Email<EMAIL_ADDRESS>
###### Abstract

Social media are decentralized, interactive, and transformative, empowering
users to produce and spread information to influence others. This has changed
the dynamics of political communication that were previously dominated by
traditional corporate news media. Having hundreds of millions of tweets
collected over the 2016 and 2020 U.S. presidential elections gave us a unique
opportunity to measure the change in polarization and the diffusion of
political information. We analyze the diffusion of political information among
Twitter users and investigate the change of polarization between these
elections and how this change affected the composition and polarization of
influencers and their retweeters. We identify “influencers” by their ability
to spread information and classify them into those affiliated with a media
organization, a political organization, or unaffiliated. Most of the top
influencers were affiliated with media organizations during both elections. We
found a clear increase from 2016 to 2020 in polarization among influencers and
among those whom they influence. Moreover, 75% of the top influencers in 2020
were not present in 2016, demonstrating that such status is difficult to
retain. Between 2016 and 2020, 10% of influencers affiliated with media were
replaced by center- or right-orientated influencers affiliated with political
organizations and unaffiliated influencers.

## Introduction

A growing number of studies have documented increasing political polarization
in the U.S. that is deeper than at any time since the American Civil War [1,
2, 3]. Partisan division over issues has increased among those affiliated with
political and news media organizations – elected representatives, party
officials, and political pundits – alongside an alarming increase in affective
polarization among voters. This two-level pattern – issue polarization among
political elites and affective polarization among voters – invites further
research on the diffusion of polarized political information between those in
positions of political influence and the larger population.

This diffusion of political information is difficult to track with traditional
survey and roll call voting data that lack relational measures. Increasing
reliance on social media for political communication is opening up
unprecedented opportunities to study the diffusion of political information
and misinformation [4] over communication networks [5]. Our study leverages
social media data from Twitter to better understand the diffusion dynamics of
news media information during the two most recent U.S. election cycles.

Twitter users are embedded in relatively stable communication networks created
by the exchange of “retweets.” A 2015 study by Metaxas et al. [6] found that
“retweeting indicates not only interest in a message, but also trust in the
message and the originator, and agreement with the message contents.” The
content of retweets makes it possible to identify information that is highly
biased, as well as the ideological direction of the bias. Using retweet data
we also can identify “influencers” who are the users with the greatest ability
to broadly propagate new information over the retweet network. Typically,
influencer tweets are highly likely to be retweeted, not only by their
followers, but also by their followers’ followers, and so on. We classify
Twitter influencers into two categories. The first includes the “affiliated”
who are associated with media or political organizations, and the other
consists of the “unaffiliated” who do not have such associations, so most
likely represent themselves or informal groups.

Our study also aims to better understand how polarization unfolds on social
media. To clarify, political scientists distinguish multiple types and levels
of polarization [7, 8, 9, 10, 11, 12]: policy polarization (extreme
differences of opinion on highly salient issues), partisan polarization
(alignment of opinions with opposing political parties), ideological
polarization (alignment of opinions with liberal vs. conservative world
views), and geographic polarization (regional alignment of opinions, e.g.,
“red state/blue state”). Each of these four types of polarization can in turn
be classified by level: elite polarization among political officials and
pundits, media polarization among news organizations, and polarization among
the underlying population as usually measured by exit polls and opinion
surveys. In this paper, we use data from social media to study ideological
polarization among the political elite, news organizations, and Twitter users
more broadly. Over the past decade, the rapid growth of Twitter, Facebook,
Reddit and other social media have transformed the communications and
information propagation landscape. Alongside traditional broadcast media and
face-to-face communication, people now have the ability to search for and
exchange information with billions of other users in a global network. Recent
studies have examined the impact of new technologies like Twitter and YouTube
on election outcomes [13, 14, 15, 16, 17, 18, 19, 20, 21, 22], including the
effects of disinformation [23, 24, 25, 26, 27, 28]. Other studies have
documented how social media platforms contribute to polarization through the
creation of echo chambers [29, 30, 31, 32, 33, 34, 35, 36, 37, 38]. In
contrast, our study focuses on the diffusion of news media information between
influencers and those whom they influence, as well as the change in
composition, popularity, and polarization among influencers and their
retweeters in the months leading up to the 2016 to 2020 U.S. presidential
elections.

To maintain the consistency between the results from 2016 and 2020 elections,
we follow the methodology of Ref. [25] to identify and classify the influences
and their retweeters in the 2020 U.S. election data. We classify tweets
containing a link to a news outlet into several news media categories
corresponding to their different political orientations. We observe that the
volume of tweets and users with a center orientation decreased from the 2016
election to the 2020 election. For each news media category, we reconstruct
the corresponding retweet network and identify the most important news media
influencers of the category by finding the most important nodes in terms of
their ability to spread information in the network. The top 25 influencers in
each news media category are then classified as affiliated with a media or
with a political organization or unaffiliated. We find that the proportion of
top influencers affiliated with news media organizations decreased in 2020,
while the proportion who were politically linked increased. This may indicate
a shift in the sources of influence over political agenda setting.
Simultaneously, the proportion of media affiliated influencers increased in
2020 in the categories containing disinformation, indicating a shift in the
sources of disinformation from informal to formal organizations. Finally, we
measure the strength of the polarization of the influencers and of their
retweeters, defined as the level of separation of the influencers’ retweeters
in two opposite clusters and find a clear, significant increase of the
polarization from 2016 to 2020.

## Results

### News media on Twitter in 2016 and 2020

We tracked the spread of political news on Twitter in 2016 and 2020 by
analyzing two datasets containing tweets posted between June $1^{\text{st}}$
and election day (November $8^{\text{th}}$ in 2016 and November
$2^{\text{nd}}$ in 2020). The data were collected continuously using the
Twitter Search API with the names of the two presidential candidates in each
of the presidential elections in 2016 and 2020 as keywords. (Had we used more
keywords targeting specific media outlets or hashtags concerning specific news
events we would risk missing election-related tweets that did not contain
references to the list of outlets or events.) The 2016 dataset contains 171
million tweets sent by 11 million users and was used in Refs. [17, 25] to
assess the influence of disinformation on Twitter in 2016. The 2020 dataset
contains 702 million tweets sent by 20 million users. Hence, we observe a
significant increase in Twitter involvement in distributing election
polarization, since in four years the number of Twitter users nearly double
and number of tweets per user more than double, increasing the total number of
tweets more than fourfold.

The classifications of news media websites presented below and used in this
paper, including “fake”, “extremely biased”, “left”, “right”, and especially
the boundaries between categories, are a matter of opinion, rather than a
statement of fact. The categorizations and labels assigned to the
corresponding classes used here originated in publicly available datasets from
fact-checking and bias rating organizations credited below. The political
views and conclusions contained in this article should not be interpreted as
representing those of the authors or their funders.

For each tweet containing a URL link, we extracted the domain name of the URL
(e.g. www.cnn.com) and classified each link directing to a news media outlet
according to this outlet’s political bias. The 2016 and 2020 classifications
rely on the website allsides.com (AS), followed by the bias classification
from mediabiasfactcheck.com (MBFC) for outlets not present in AS (both taken
as of January 7 2021 for the 2020 classification). We classified URL links in
five news media categories for outlets that mostly conform to professional
standards of fact-based journalism: right, right-leaning, center, left-leaning
and left. We also include three additional news media categories to include
outlets that tend to disseminate disinformation: extreme-right bias, extreme-
left bias and fake news. Websites in the fake news category have been flagged
by fact-checking organizations as spreading fabricated news or conspiracy
theories, while websites in the extremely biased category have been flagged
for reporting controversial information that distorts facts and may rely on
propaganda, decontextualized information, or opinions misrepresented as facts.
A detailed explanation of the methodologies used by AS and MBFC for rating
news outlets and of the differences in classification between 2016 and 2020 is
given in the Methods section. The full lists of outlets in each category in
2016 and 2020 are given in SM Tabs. S1 and S2. In the 2016 dataset, 30.7
million tweets, sent by 2.3 million users, contain a URL directed to a media
outlet website. The 2020 dataset contained 72.7 million tweets with news links
sent by 3.7 million users. This number reveals remarkable drop of fraction of
flow of tweets from users associated with media form $18\%$ in 2016 to $10\%$
so nearly half lower. This came from mainly from smaller growth of
productivity of media affiliated users.

Figure 1: Distribution of news media links in 2016 and 2020, by news media
category. Panels A and B show the fractions of tweets and users that sent
tweets with a URL pointing to a website belonging to one of the categories.
Users are classified in the category in which they posted the most links. For
the users that have at least two links classified, panels C and D report the
fraction of links across categories as a function of the users’ main
categories.

The fractions of tweets and users who sent a tweet in each of the news media
categories are shown in Fig. 1 A and B (the numbers are reported in SM Tab.
S3) along with other statistics about the activity of users in each category.
Between 2016 and 2020, these fractions decreased most in the center category
and increased most in the left-leaning category, with a smaller increase in
the fractions in the right-leaning category. The shift away from the center
may indicate the increasing polarization, both among users as well as media
outlets. However, most of the decrease in the fraction of center media outlets
reflects the shift of CNN.com, which was categorized by AS as center in 2016
and as left-leaning in 2020, combined with CNN accumulating more than twice
the number of tweets in 2020 than the top outlet of the center category
(thehill.com) (SM Tab. S2).

The fraction of tweets in the fake and extremely biased category, representing
outlets that were most susceptible to sharing disinformation, decreased from
10% to 6% for fake news and from 13% to 6% for extremely right-bias news. The
number of users who shared those tweets also decreased for extremely right-
biased news (from 6% to 3%) but not for fake news (which remained at 3%) (SM
Tab. S3). The fraction of tweets in the extremely-left bias category is very
small (2% in 2016 and even less, 0.05% in 2020).

Fig. 1 C and D shows the fraction of URLs across categories as a function of a
user’s modal category for users that posted at least two links in our
datasets. The analysis reveals two clusters in 2016 and 2020, one with
categories from the right and fake news (fake news, extreme-right bias news,
right news and right-leaning news) and the second one with categories from the
center and left (center news, left-leaning news, left news, extreme-left bias
news). These two clusters can be interpreted as two echo chambers.
Asymmetrical patterns in Fig. 1 C and D reveal that users in the right wing
echo chamber also link to a very limited number of left wing media outlets,
but that the opposite relation does not occur. This is consistent with
asymmetry between left-leaning and right-leaning users in social media
observed in previous studies [38, 25, 39].

In order to estimate the volume of tweets sent from automated accounts such as
bots, we counted the number of tweets sent from unofficial Twitter clients,
e.g., Twitter clients other than the Twitter Web client, Android client,
IPhone client or other official clients. Unofficial Twitter clients include a
variety of different applications used to automate all or part of an account
activity, such as third party applications used typically by brands and
professionals (e.g. SocialFlow or Hootsuite) or bots created with malicious
intentions [17].

The overall fraction of tweets sent from unofficial clients was 8% in 2016 and
1% in 2020. A similar drop over the same period was observed in the average
activity of these users (see SM Tab. S3). This decrease could be attributed in
part to measures taken by Twitter to limit the virality of disinformation. Our
results show that while the relative volume of tweets linking to
disinformation websites dropped approximately by a half in 2020 compared to
2016, the fraction of users sharing fake news decreased significantly (Fig. 1
A and B and Tab. S3).

Figure 2: Shifts of users across news media categories from 2016 to 2020. The
size of each category in 2016 corresponds to the number of unique users in the
category in 2016 (Fig. 1). The shift from one category to another is
proportional to the fraction of users that were classified in 2016 and in 2020
in the two respective categories.

To understand how users shifted between categories from 2016 to 2020, we track
users that are present in both election datasets and in both years are
classified into the category in which they posted the most tweets in each
year. Fig. 2 shows the resulting shifts. The two largest of them are of users
that were in the center and left news category in 2016 and shifted to the
left-leaning category in 2020. This reflects the consolidation of the left-
leaning category as the largest in 2020, with the three most widely shared
outlets: New York Times, Washington Post and CNN (SM Tab. S2). We also observe
a large fraction of users in the fake and extremely biased news category in
2016 that moved to the right news category in 2020. However, these user shifts
also reflect the change in the classification of media outlets from 2016 to
2020. We infer the ideological position of Twitter users without relying on
the news outlet classification in section Polarization among Twitter users,
and show that the resulting positions are highly correlated with the user
positions computed using the news categories in which they posted.

Instead, we use directed edges to track the flow of the information contained
in the tweet from the node that originated the information to those who
retweet it.

### News media influencers

To capture the dynamics of information diffusion, we reconstruct retweet
networks corresponding to each news media category. We add a link, or directed
edge, going from node $v$ to node $u$ in the news network when user $u$
retweets the tweet of user $v$ that contains a URL linking to a website
belonging to one of the news media categories. With this convention, the
direction of the link represents the direction of the influence between
Twitter users. We do not include multiple links with the same direction
between the same two users or self-links (when a user retweets their own
tweets). The in-degree of a node is the number of links that point inward to
the node and its out-degree is the number of links that originate at a node
and point outward to other nodes. With our convention, the in-degree of a user
is equal to the number of users they retweeted at least once and their out-
degree is the number of users who have retweeted them at least once. The
higher a node’s out-degree, the greater its local influence. The
characteristics of the retweet networks are given in SM Tab. S4.

In each retweet network, we use the Collective Influence (CI) algorithm [40]
to find the best spreaders of information, i.e. the influencers of the
corresponding news media category. Specifically, the CI algorithm finds the
minimal set of nodes that can collectively reach the entire network when
information diffuses according to a linear threshold model. The CI algorithm
considers influence as an emergent collective property, not as a local
property such as the node’s out-degree. It does this by finding the smallest
set of nodes needed for global cascades. Accordingly, the CI algorithm is able
to rank super-spreaders of information in social networks [41, 42, 25].

Figure 3: Retweet networks formed by the top 30 influencers within each media
category, by year. The 2016 network (upper panel) was generated from 2016 data
using the same algorithm as used in [25] but with different parameters to ease
its comparison with the bottom panel generated from 2020 data. The arrows show
the directions of links between users, from the source of influence (the
original poster) to the recipient (the retweeter). The size of a node is
proportional to its out-degree in the complete combined network, i.e., the
number of different users that have retweeted the node at least once with a
URL directing to a media outlet. The color of a node indicates the news media
category with which the node is affiliated. Nodes ranked in the top 30 of
multiple categories are represented by pie charts where the size of each slice
is proportional to their CI${}_{\textrm{out}}$ ranking (i.e. the node’s
collective influence). Both networks are visualized using a force-directed
graph layout. The tables on either side of the networks show the top five
users in each news media category. The number in green to the left of each
user is their unique index, used to label the user’s node in the network.
Users ranked in the top 30 for multiple news media categories have colored
superscripts, indicating the rank and media classification of their other top
five positions. Verified users are indicated by a checkmark $\checkmark$. In
the 2020 tables, a user’s 2016 rank is displayed with the purple number to the
left of their 2020 rank. Three usernames in the top 10 changed between 2016
and 2020: @DRUDGE_REPORT became @NEWS_MAKER, @HuffingtonPost became @HuffPost,
and @TruthFeedNews became @TAftermath2020. Those users will have their new
handle displayed in the 2016 tables for consistency (as well as in Figs. 5 and
6).

Here, we use a directed version of the algorithm to identify the super-
spreaders of information as the nodes with the highest CI${}_{\text{out}}$ to
be able to compare results from both elections [25]. The 2016 influencers’
rankings are shown in the upper panel in Fig. 3 for the top five influencers,
and in SM Tab. S5 for the top 25 influencers. Analysis of these results
reveals that traditional news influencers were mostly journalists with
verified Twitter accounts linked to traditional news media outlets. In
contrast, fake and extremely biased news are sent mainly by influencers whose
accounts are unverified or deleted, with deceptive profiles and much shorter
life-span on Twitter than traditional media influencers. However, some of
these influencers, despite their unknown, non-public nature, still played a
major role in the diffusion of disinformation and information on Twitter [25].

The results from analysis of 2020 data are shown in the bottom panel of Fig.
3. For influencers that persisted from 2016, their previous position in 2016
is listed in purple parentheses (see also SM, Tab. S6). Those influencers are
often highly ranked in both the 2016 and the 2020 analyses. Among the union of
top 100 influencers from each category in 2020 (representing 598 unique users)
150 were already in the top 100 of one category in 2016. Yet, this means that
75% of the top 100 influencers in 2020 are new to such high ranking.

The CI algorithm operates on the unweighted retweet networks. To verify that a
ranking computed on the weighted networks would not produce significantly
different results, we compare the CI ranking with the ranking obtained from
the PageRank algorithm applied to the weighted networks. The comparison
reveals a strong agreement, especially for highly-ranked users (SM Fig. S1).

Fig. 3 shows the retweet networks for each news media category in 2016 and
2020, among communities formed by the union of the top 30 influencers for each
category. The two force directed network layouts are computed using the same
parameters and show two main clusters, with the right-biased and fake news
influencers in one cluster and the left-biased influencers in the other. The
increased separation in 2020 is notable. In 2016 the center influencers are
mostly between the two clusters; in 2020 the separation between the two
clusters increased and only a few influencers remain within a central position
(e.g. @thehill). We quantify the polarization of the full set of top 100
influencers and of their retweeters, using all the retweets between them, in
detail in the next section.

Fake and extremely biased news are sent mostly by influencers whose accounts
are unverified or deleted, with fake news seeing a significant increase in
deleted influencer accounts from two in the top 25 in 2016 to eight in 2020
(see SM Tabs. S5 and S6). Conversely, the extreme right-biased news in 2020
consisted primarily of verified influencers that grew from 15 verified in the
top 25 in 2016 to 23 in 2020.

Using a manual labeling process (see Methods), we classify the top 25
influencers of each news media category for 2016 and 2020 as affiliated with
media or political organizations, or unaffiliated, in order to observe the
makeup of influencer types for these categories. An influencer affiliated with
a media organization could be a media company or official media outlet (e.g.
@FoxNews), or a writer, reporter, consultant, or other individual who has
directly corresponded with a media outlet in an officially recognized capacity
(e.g. @joelpollak). An influencer affiliated with a political party could be a
politician (e.g. @JoeBiden), a political campaign platform or an affiliate of
the platform, or someone who officially represents an aspect of U.S. politics
(e.g. @joncoopertweets). We also split the unaffiliated category into two
subcategories: independent and “other.” An independent influencer is an
influencer not officially affiliated with any media or political platforms
(e.g. @amberofmanyhats). The “other” category represents influencers whose
accounts have no descriptions or context that could be used to identify them.

Figure 4:  Reshuffling of distribution of the top 25 influencer types from
2016 to 2020, by news media category. Influencers are classified as affiliated
with a media organization, political organization, independent, or other (e.g.
unidentified).

The fractions of influencers in these categories are shown in Fig. 4. It
reveals that unaffiliated influencers are more common in the fake and extreme-
bias categories, while affiliated influencers are more common in the other
news categories. A similar trend is evident in the fractions of verified and
unverified influencers found in these categories (SM Tab. S6), as fake and
extreme-bias news categories generally contain fewer verified influencers. In
addition, media-affiliated influencers seem to have a greater presence in the
left, left-leaning, and center news categories compared to their counterparts.
Interestingly, the number of media-affiliated influencers within most of the
categories actually decreases from 2016 to 2020. The exceptions are the
extreme-right bias and fake news categories, which actually increased in
media-affiliated influencers, while the extreme-right bias category also
increased in politically-linked influencers. This indicates a shift in
polarization of influencers affiliated with right-biased political and media
organizations toward the extreme-right bias and fake news, as well as the
emergence of new media-affiliated influencers in these categories. We discuss
these changes in more detail below. In addition to changes in user types and
verified users from 2016 to 2020, we observe a significant reshuffle of the
ranking of influencers. Fig. 5 shows the change in rankings of the top 10
influencers in left and left-leaning, right and right-leaning and extreme-
right bias and fake news categories. The ranking reshuffle in the center news
category is shown in SM Fig. S2.

Figure 5:  Change in influencers rankings from 2016 to 2020. Influencers
ranked in the top 10 in at least one news media category in 2016 or 2020 are
shown. The 2016 rankings are displayed to the left of the username, with 2020
rankings on the right. For each user only one shift is shown. Its color
changes from the user’s highest ranked news media category in 2016 to such
color for 2020. Each panel shows the change over time between two news media
categories.

The comparison reveals several interesting changes between 2016 and 2020.
First, we see that highly influential users rise from obscurity. Across all
categories, a set of previously unranked or very low-ranked users break in to
the top-10 rankings. These users include, for example, @TeaPainUSA, @svdate,
@kylegriffin1, @marklevinshow, @DavidHarrisJr, etc. Considering all unique
users in the top 25 influential users (across all categories of news media),
we see that 58% came from outside the top 100 influential users in 2016.
However, most of these newly influential users are related in some way to
media or political organizations, while 28% of these new influencers are
independent.

Observing the change in rankings by news media category, we see that
right/right-leaning and extreme-right bias/fake news categories have a
significantly higher fraction of top 10 influencers who were previously
outside the top 50, compared to the change in rankings among the groups in
left/left-leaning news categories. All categories show a large number of
influencers falling out of the top 50 from 2016 to 2020, and in the case of
the left news influencers, we see their former positions filled by users who
were much less influential in 2016. The influencers with extreme right bias
and fake news affiliations show the most volatility with regards to retaining
top-10 influencer positions, with many top-10 influencers in 2016 ranked below
50 in 2020 (or were even banned from Twitter, like @RealAlexJones).

The change of classification of some news media outlets is also reflected in
the category shifts of their Twitter accounts. In particular, @CNN and
@politico – previously the first and third highest ranked influencers in the
center category in 2016 – shifted to left-leaning. Such shifts of large and
influential media influencers across news categories indicates the increased
content polarization on Twitter. A shift of media-affiliated influencers from
the right to the extreme right is also visible (e.g. @DailyMail,
@JudicialWatch, @marklevinshow), as well as the emergence of new-media
affiliated influencers in these categories (e.g. @newsmax, @OANN or
@RaheemKassam). In contrast to the shift to the extremes among large media
influencers, the center rankings remained fairly consistent between 2016 and
2020 (SM Fig. S2). Some new users rose from low ranks to fill in the gaps,
including @JoeBiden, but only one user dropped out of the top 50 entirely, and
the remaining shifts are internal to these top-ranked users.

### Polarization among Twitter users

The evolution of influencers across different news media categories (Figs. 1
and 2) suggests an increased polarization in the relations among influencers
between 2016 and 2020. Here we broaden the scope of polarization analysis to
the Twitter users who are consuming and retweeting the influencers’ content.
For the 2016 and 2020 data, we consider the union of the top 100 influencers
of each news media category as a single set representing the most influential
users covering the entire news media ideological spectrum for the target year.
For this analysis we use all the retweets in our datasets, not only those
containing a link to a news outlet, and remove the ones sent from unofficial
Twitter clients to capture only tweets sent by humans. Using these influencers
as nodes, we create two fully connected similarity networks derived from the
2016 and 2020 Twitter network, respectively. An edge between any two
influencers in the created networks represents similarity of the number of
retweets of these two influencers for every user in the corresponding Twitter
network (see Methods for more details). In both similarity networks, a
community detection algorithm found two communities. One contained influencers
affiliated with news media in the center, left-leaning and left news
categories, while the other contained those affiliated with news media in the
right-leaning, right and fake news categories. This indicates that influencers
separate their user bases according to the content they generate. We
illustrate this separation in SM Fig. S3 that shows a sample of the 2020
similarity network. To quantify the difference in community separation and,
subsequently, polarization, between the two networks, we measured the
modularity and normalized cut between communities (see Methods for details).

The modularity for the 2020 network was 0.39 with a standard error (SE) of
0.01, versus 0.365 (SE = 0.007) in 2016, indicating more closely knit
communities in 2020, with stronger in-community ties and weaker between-
community ties. Consistent with the increase of community modularity, the
average normalized cut of 0.36 (SE = 0.04) in 2016 decreased to 0.128 (SE =
0.005) in 2020. To interpret this change, we note that on average, each node
in the 2016 similarity network had $64\%$ of in-community edges and $36\%$ of
across-community edges. The latter fraction decreased to $13\%$ in 2020,
dropping nearly three times lower than it was in 2016. This indicates much
stronger separation of these communities in the later election. We also
computed the above metrics on networks generated from user quote similarity in
order to show that retweets are the strongest form of endorsement of
influencer content, and subsequently the best approach for our analysis (see
SM Tab. S7).

To quantify and compare the polarization not only among Twitter influencers
but also among the users, we infer the ideology of Twitter users based on the
ideological alignment of political actors they follow [43, 32]. The bipartite
network of followers is then projected on a one dimensional scale using
correspondence analysis [44, 45], which applies a SVD decomposition of the
adjacency matrix standardized to account for the differences in popularity and
activity of the influencers and their followers (see Methods for details). Two
users are close on the resulting latent ideology scale if they follow similar
influencers. This method has been shown to produce ideological estimates of
the members of the U.S. Congress highly correlated with ideological estimates
based on roll call voting similarity such as DW-NOMINATE [43].

For 2016 and 2020, the data for the analysis consists of the union of the top
100 influencers of each news media category and the sets of users that
retweeted at least three different influencers (considering all tweets in our
datasets, not only the ones with URLs). Following the finding in [6] that
“retweeting indicates not only interest in a message, but also trust in the
message and the originator, and agreement with the message contents,” we
interpret retweeting a form of endorsement of the content being retweeted.
Twitter offers other types of interactions allowing users to comment on the
content, such as quote tweets and replies. The ratio of quotes to retweets of
users to influencers was very stable and small ($<5\%$) in 2016 and 2020, for
users on the left and right sides of the latent ideology (SM Tab. S8A), which
motivated our focus on retweets to infer the ideology of users. We note that
the ratio of quotes to retweets from users of one side of the ideology
spectrum to influencers of the other side increased from 2016 to 2020,
indicating an increased usage of quotes to comment on tweets from influencers
of the opposite side. However, the overall usage of quotes over retweets
remained small (SM Tab. S8B). We extract the coordinates of each user on the
first dimension of the results of the correspondence analysis applied to the
weighted network of retweets between the users and the influencers (see
Methods for the details and robustness checks that we performed). Finally, for
2016 and 2020, the coordinates of all users are standardized to a mean of zero
and a standard deviation of one. Two users are close together on the latent
ideology scale if they tend to retweet similar influencers. The influencers’
latent ideological positions are then computed as the median of their
retweeters’ positions.

Figure 6: Latent ideology scale of influencers and their retweeters in 2016
(left) and 2020 (right). The latent ideology of the top five influencers of
each category is shown as a box plot representing the distribution of the
ideology of the users who retweeted them. The distributions for the users are
shown in green, and the distributions for the top 100 influencers of each news
media category (computed as the median of the ideology of their retweeters)
are displayed in purple. Box plots indicate the 25% and 75% percentiles of the
distributions with whiskers indicating the 5% and 95% percentiles. Pie charts
next to the influencers’ names represent the news categories to which they
belong (weighted by their respective CI ranks in each category).

Fig. 6 shows the result of this analysis. The latent ideology of the top five
influencers of each category is shown as a box plot representing the
distribution of the ideology of the users who retweeted them. The distribution
of ideology positions of the users and of the influencers, displayed in green
and purple, respectively, shows that polarization increased between 2016 and
2020. This is confirmed by a Hartigans’ dip test (HDT) for unimodality, which
measures multimodality in a sample by the maximum difference, over all sample
points, between the empirical distribution function, and the unimodal
distribution function that minimizes that maximum difference [46]. For the
user distribution, the test statistics is $D=0.1086$ (95% CI: [0.108,0.109])
in 2016 and $D=0.1474$ (95% CI: [0.1471,0.1477]) in 2020. For the influencer
distribution, the test statistics is $D=0.17$ (95% CI: [0.16,0.20]) in 2016
and $D=0.21$ (95% CI: [0.19,0.23]) in 2020. All tests reject the null
hypothesis of a unimodal distribution with $p<2.2\times 10^{-16}$ and the 95%
confidence intervals are computed from 1000 bootstrap samples using the bias-
corrected and accelerated method. Increasing values of the test statistic
indicates distributions that increasingly deviate from a unimodal
distribution, corroborating the growing division found in the similarity
networks.

To understand if the measured increase in polarization is due to the arrival
of new users and influencers in 2020, we repeat this analysis including only
users (shown in SM Fig. S4), only influencers (Fig. S5) or only users and
influencers (Fig. S6) that were active during both elections. In all cases we
observe an increase of the Hartigans’ dip test (HDT) statistics (see SM Fig.
S7 and Tab. S9) indicating that the increased polarization is not only due to
the departure and arrival of new users between elections but also to a change
of behavior of the users that remained. The largest increase in HDT for the
user distribution is obtained when all users of 2016 and 2020 and only
influencers that were present during both years are considered ($+0.08$). This
setting also corresponds to the smallest increase of the dip test of the
influencer distribution ($+0.01$, within 95% CI), suggesting that the new
influencers of 2020 have more polarized ideologies than the influencers who
remained from 2016 and that the increased polarization of the users is due in
large part to the arrival and departure of users between elections (SM Fig. S7
and SM Tab. S9).

Figure 6 reveals a clear increase in polarization of the users and influencers
in 2020 compared to 2016 and an alignment of their latent ideologies in two
distinct groups, mirroring the news media classification groupings seen in
Fig. 3 and Fig. S3. The box plots show that the distributions of users
retweeting influencers became more concentrated in 2020, with two clear
opposite poles and fewer influencers having a user base bridging opposite
ideologies. These results independently confirm the shift of news outlets and
influencers from the center to the right and left observed using the news
media classifications by external sources. Indeed, we find an extremely high
correlation (above $0.90$ for 2016 and 2020) between the users’ latent
ideology position and their left- or right-leaning distribution computed using
the news media categories in which they posted (see Methods). This high
correlation indicates that the shift in bias observed at the level of the
media outlets is also present at the level of the users’ retweeting pattern
and serves as an independent validation of the media outlet classification.

## Discussion

This paper uses Twitter retweets to study polarization among influencers and
those they influence in the months leading up the 2016 and 2020 U.S.
Presidential elections. Multiple analyses confirm a robust pattern of
increasing division into opposing echo chambers, largely due to the arrival of
new, more polarized influencers and users in 2020.

Our study also provides unique insights on the rapidly evolving news media
landscape on Twitter. Among the top 100 influencers aggregated across all news
media categories in 2020, seventy-five percent were not present in 2016,
demonstrating how difficult it is to retain influencer status. Most of the
influencers who appeared in 2020 were associated with prominent media or
political party organizations. The number of influencers affiliated with media
organizations declined by 10% between 2016 and 2020, replaced mostly by
influencers affiliated with center and right-leaning political organizations.
This change in the news media landscape on Twitter from 2016 to 2020 indicates
a shift in the relative influence of journalists and political organizations.
On the other hand, in the extremely right biased and fake news categories, new
professional media have emerged and taken the place of mostly independent
influencers.

Future research should build on this structural analysis by examining the
content of the messages. Content analysis is needed to distinguish between
tweets that are positively and negatively quoted and to develop measures of
influence that go beyond the ability to attract attention from retweeters. For
example, an urgent question to answer is whether the influence of unaffiliated
Twitter influencers goes beyond being news spreaders: do they also have the
ability to set the issue agenda? Our study is limited to describing what
happened on Twitter. Future research should analyze message content for clues
about the ability of influencers to mobilize voters and social movements
offline. We also focused on the flow of information from influencers to those
who retweet them. Future research should investigate how the actions of the
retweeters and followers affect the influencers, how influencers form networks
across types of media, and what are the offline consequences of polarization
of Twitter influencers and users, including the impact on voting. It should
also be possible to monitor interactions on other social media and during non-
election periods to permit finer grained analysis of the new entrants.

## Methods

### News media URL classification

The website www.allsides.com (AS) rates media bias using a combination of
several methods such as blind surveys, editorial review, third party analysis
(e.g. academic research), independent review and community feedbacks (see
www.allsides.com/media-bias/media-bias-\\\rating-methods for a detailed
explanation of their methodology). The website mediabiasfact\\\check.com
(MBFC) scores media bias by evaluating wording, sourcing, and story choices as
well as political endorsement (see mediabiasfactcheck.com/methodology). MBFC
is maintained by a small independent team of researchers and journalists,
offers the largest set of biased and inaccurate news sources among five fact
checking datasets [47], and is widely used for labeling bias and veracity of
news sources (e.g., in [48, 49, 50]).

To be consistent with the results from 2016 [25], we discard as insignificant
outlets that accumulate less than $1\%$ of the cumulative number of tweets of
the more popular outlets in each category. Removing uniformly insignificant
outlets from all categories also ensures that the tweet volume in each
category is independent of the number of outlets classified in this category
by AS and MBFC. The full lists of outlets in each category in 2016 and 2020
are given in SM Tabs. S1 and S2. AS and MBFC updated their bias classification
for several outlets between 2016 and 2020, changing the classification used in
our analyses as well. For example, CNN Web News was classified in the center
category in 2016 by AS and then in the left-leaning category in 2020,
reflecting a bias shift occurring during this time (see
www.allsides.com/blog/yes-cnns-media-\\\bias-has-shifted-left).

In Ref. [25], the fake news and extreme-bias categories were based on the
classification of a team of media experts (available at
github.com/alexbovet/opensources) and was cross-checked using the factual
reporting scores from MBFC. As the classification source from 2016 was not
updated in 2020, we use the list of outlets classified as “questionable
sources” from MBFC as a reference for 2020. MBFC describes a questionable
source as one “that exhibits one or more of the following: extreme bias,
consistent promotion of propaganda/conspiracies, poor or no sourcing to
credible information, a complete lack of transparency and/or is fake news.”
MBFC rates the factual reporting of each source on a scale from 0 (very high)
to 10 (very low) based on their history of reporting factually and backing up
claims with well-sourced evidence. Outlets with a level of “low” (score of 7,
8 or 9) or “very low” (score of 10) are classified in the fake news category
while outlets with a “mixed” level (score of 5 or 6) are classified in the
extremely biased category. No outlets in the disinformation categories have a
level higher than “mixed.” A “low” or “very low” factual reporting level on
MBCF corresponds to sources that rarely, or almost never use credible sources
and “need to be checked for intentional fake news, conspiracy, and
propaganda.” A “mixed” level is assigned to sources that “do not always use
proper sourcing or source to other biased/mixed factual sources.” We also
verify that all outlets in the extremely biased category have a “bias”
reported on MBFC of “right”, “extreme right”, “left“ or “extreme left.”

We identify in our datasets (we give the top hostname as an example in
parenthesis) for the fake news category: 16 hostnames in 2016 (top:
thegatewaypundit.com) and 20 hostnames in 2020 (top: thegatewaypundit.com),
for the extremely biased (right) category: 17 hostnames in 2016 (top:
breitbart.com) and 10 hostnames in 2020 (top: breitbart.com), for the
extremely biased (left) category: 7 hostnames in 2016 (top: dailynewsbin.com)
and 7 hostnames in 2020 (top: occupydemocrats.com), for the left news
category: 18 hostnames in 2016 (top: huffingtonpost.com) and 18 hostnames in
2020 (top: rawstory.com), for the left-leaning news category: 19 hostnames in
2016 (top: nytimes.com) and 19 hostnames in 2020 (top: nytimes.com), for the
center news category: 13 hostnames in 2016 (top: cnn.com) and 13 hostnames in
2020 (top: thehill.com), for the right-leaning news category: 7 hostnames in
2016 (top: wsj.com) and 13 hostnames in 2020 (top: nypost.com), for right news
category: 20 hostnames in 2016 (top: foxnews.com) and 19 hostnames in 2020
(top: foxnews.com). The full lists of outlets in each category in 2016 and
2020 are given in SM Tabs. S1 and S2.

### Influencer type classification

For each of the years 2016 and 2020, we manually classified the top-25
influencers in each news media category as affiliated to a media organization
or a political organization, or unaffiliated (classified either as an
independent user or as an unidentified “other” user). The manual labeling
procedure was as follows: Eight of the authors were randomly assigned a subset
of the union of the top-25 influencers in these category lists to
independently classify, such that each subset was examined by three different
authors. Each author was shown the account name of the influencer along with
descriptions, posts, and all available non-Twitter information such as their
Wikipedia entry. Each influencer was then assigned their category based on the
majority vote of the three independent classifications.

### Similarity network analysis

We start by creating for each influencer $i$ a vector $\vec{S^{i}}$ of size
$U$, which stands for the number of users in our dataset. We used a set of
$588$ influencers for the 2016 dataset, and a set of $661$ influencers for the
2020 dataset. An index $u$ is assigned to the specific user. The vector
element $s^{i}_{u}$ defines the number of times user $u$ has retweeted
influencer $i$. Then, we create the adjacency matrix $\mathbf{A}$ of size
$I\times I$ for our similarity networks by setting $a_{i1,i2}$ to the cosine
similarity between vectors $\vec{S^{i_{1}}}$ and $\vec{S^{i_{2}}}$. It follows
that the higher the cosine similarity, the more users have the similar number
of retweets for influencers $i_{1},i_{2}$.

We detect communities in the similarity network using the Louvain algorithm
[51]. In the similarity networks for both election years, we found two
communities. Using the accounts of influencers in each community, we found
that both election years one community contains influencers primarily
associated with fake and right-biased news categories, while the other
contains influencers from center and left-biased news categories. This split
coincides with an underlying division among the Twitter user bases in the
content they propagate.

We quantify the severity of this split using two measures of separation
between communities. First is modularity that computes the sum of difference
between the fraction of edges within each community and such fraction expected
within this community in a random network with the same number of nodes and
edges. This metric has a range of $[-0.5,1]$ [52]. A positive value indicates
the presence of communities separated from each other. The closer the
modularity is to 1, the stronger communities are separated.

The second measure uses the normalized cut, which is the sum of the weights of
every edge that links a pair communities divided by the sum of the weights of
all edges. The result has a range of $[0,1]$ where the smaller the value, the
stronger the separation among communities.

### Latent ideology estimation

The latent ideology estimation follows the method developed in [43, 32]
adapted to using retweet interactions instead of following relations. As in
[32], we use correspondence analysis [44] (CA) to infer ideological positions
of Twitter users.

The adjacency matrix, $\mathbf{A}$, of the retweet network between the
influencers and their retweeters is the matrix with element $a_{ij}$ equal to
the number of times user $i$ retweeted influencer $j$. We only select tweets
that have been sent from the official Twitter client in order to limit the
presence of bots and professional accounts and we also remove users that show
a low interest in the U.S. elections by removing users that retweeted less
than three different influencers. For the 2016 data, the matrix $\mathbf{A}$
has 751,311 rows corresponding to distinct users, 593 columns corresponding to
influencers and the total number of retweets equal to 39,385,772. For the 2020
data, the matrix $\mathbf{A}$ has 2,034,970 rows corresponding to distinct
users, 591 columns corresponding to influencers and the total number of
retweets equal to 153,463,788.

The CA method is executed in the following steps [45]. The matrix of
standardized residuals of the adjacency matrix is computed as
$\mathbf{S}=\mathbf{D}^{-1/2}_{r}(\mathbf{P}-\mathbf{r}\mathbf{c})\mathbf{D}^{-1/2}_{c}$,
where $\mathbf{P}=\mathbf{A}(\sum_{ij}a_{ij})^{-1}$ is the adjacency matrix
normalized by the total number of retweets, $\mathbf{r}=\mathbf{P}\mathbf{1}$
is the vector of row sums, $\mathbf{c}=\mathbf{1}^{T}\mathbf{P}$ is the vector
of column sums, $\mathbf{D}_{r}=\text{diag}(\mathbf{r})$ and
$\mathbf{D}_{c}=\text{diag}(\mathbf{c})$. Using the standardized residuals
allows the inference to account for the variation of popularity and activity
of the influencers and the users, respectively [32]. Then, a SVD is computed
such that $\mathbf{S}=\mathbf{U}\mathbf{D}_{\alpha}\mathbf{V}^{T}$ with
$\mathbf{U}\mathbf{U}^{T}=\mathbf{V}\mathbf{V}^{T}=\mathbf{I}$ and
$\mathbf{D}_{\alpha}$ being a diagonal matrix with the singular values on its
diagonal. The positions of the users are given by the standard row
coordinates: $\mathbf{X}=\mathbf{D}_{r}^{-1/2}\mathbf{U}$ where we only
consider the first dimension, corresponding to the largest singular value.
Finally, the ideological positions of the users are found by standardizing the
row coordinates to have a mean of zero and a standard deviation of one. The
ideological position of the influencers is given by the median of the weighted
positions of their retweeters.

We tested the robustness of our method by varying the way we construct matrix
$\mathbf{A}$ as follow: 1) removing entries with weight 1 to discard relations
showing a weak ideological alignment; 2) considering the logarithm of the
number of retweets as weight for influencer for a sublinear relation between
the number of retweets and the strength of ideology alignment; 3) considering
a random subsample of the 2020 retweet data of the same size than the 2016
retweet data to control for a potential effect of the difference in sizes of
the two datasets. All of these robustness tests match the results of our
initial method with correlation coefficients between the user position
distributions in the robustness tests and in the initial configuration at
above 0.995. We also compare the users’ latent ideology distribution with the
users average leaning distribution and find a correlation above 0.90 for 2016
and 2020. The average leaning of users is computed for all users having at
least three tweets classified in at least one news media category and
estimated as the weighted average of the news media category positions, given
as: fake news = 4/3, extreme-right bias = 1, right = 2/3, right-leaning = 1/3,
center = 0, left-leaning = -1/3, left = -2/3, extreme-left bias = -1.

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### Author Contributions

SF, HAM and BKS conceived the research; BKS, HAM and AB designed and
supervised the research; AB and JF coordinated and supervised the analysis;
SF, MWM, AB, HAM and BKS defined the scope of the paper; JF, AG, BC, ZZ, MS
and AB processed the collected data; all authors analyzed the results’ AB, JF,
MWM, and BKS wrote the first draft; and all authors edited and approved the
paper.

### Author Declaration

All authors declare no competing interests.

### Acknowledgments

JF, BC, and BKS were partially supported by the Army Research Office (ARO)
under Grant W911NF-16-1-0524 and by DARPA under Agreements W911NF-17-C-0099
and HR001121C0165. HAM was supported by NSF DMR-1945909.