# Partisan Asymmetries in Exposure to Misinformation

Ashwin Rao, Fred Morstatter, Kristina Lerman
Information Sciences Institute
University of Southern California

###### Abstract

Health misinformation is believed to have contributed to vaccine hesitancy
during the Covid-19 pandemic, highlighting concerns about the role of social
media in polarization and social stability. While previous research has
identified a link between political partisanship and misinformation sharing
online, the inter- action between partisanship and how much misinformation
people see within their social networks has not been well studied. As a
result, we do not know whether partisanship drives exposure to misinformation
or people selectively share misinformation despite being exposed to factual
content. We study Twitter discussions about the Covid-19 pandemic, classifying
users ideologically along political and factual dimen- sions. We find partisan
asymmetries in both sharing behaviors and exposure, with conservatives more
likely to see and share misinformation and moderate liberals seeing the most
factual content. We identify multi-dimensional echo chambers that expose users
to ideologically congruent content; however, the in- teraction between
political and factual dimensions creates conditions for the highly polarized
users—hard- line conservatives and liberals—to amplify misinformation. Despite
this, misinformation receives less at- tention than factual content and
political moderates, who represent the bulk of users in our sample, help
filter out misinformation, reducing the amount of low-factuality content in
the information ecosystem. Identifying the extent of polarization and how
political ideology can exacerbate misinformation can po- tentially help public
health experts and policy makers improve their messaging to promote consensus.

## Introduction

The growing popularity of social media as a source of news for a large portion
of the population (Pew 2018) has raised concerns about the quality and
validity of information being shared online and its effect on polarization,
which refers to division of the public into two groups with sharply
contrasting opinions or beliefs (Levy 2021; Van Bavel et al. 2021). These
concerns have only grown in urgency with the emerging evidence that social
media enables the spread of misinformation and politically polarized content
about the Covid-19 pandemic, its toll, mitigation measures, and the efficacy
of interventions, therapies and vaccines (Jiang et al. 2021; Rao et al. 2021).
According to a Pew Report (Pew 2020), political ideology explains a partisan
divide in attitudes about Covid-19 and compliance with health guidelines
(Gollwitzer et al. 2020). Since effective response to the pandemic requires
collective action, e.g., mass vaccination to achieve herd immunity, social
media can exacerbate public health impacts of the pandemic by deepening
societal divisions and amplifying health misinformation (Roozenbeek et al.
2020; Chen et al. 2021; Memon and Carley 2020), thereby hindering consensus.

Multiple studies have examined how misinformation and “fake news” are shared
online (e.g., see Grinberg et al. (2019); Vosoughi, Roy, and Aral (2018)),
focusing on methods to automatically recognize misinformation (Pennycook and
Rand 2019a) and characterize people who spread it (Pennycook and Rand 2019b).
Online polarization has been a research topic for an even longer time period.
Studies have shown that people share information that aligns with their
attitudes and political beliefs (Levy 2021). These attitudes can be measured
by analyzing online activity traces, based on the text of the messages people
share (Conover et al. 2012) or the links to news sources embedded in their
posts (Rao et al. 2021). People also seek out information sources that are
consistent with their beliefs (Knobloch-Westerwick and Meng 2009), following
and retweeting social media partisans who have similar ideology to their own
(Barberá et al. 2015; Badawy, Ferrara, and Lerman 2018). These activities
facilitate the development of “echo chambers,” which surround people with
like-minded peers who confirm their pre-existing attitudes and beliefs.
Studies have explored political echo chambers and measured their effects
(Cinelli et al. 2021; Bakshy, Messing, and Adamic 2015; Nikolov, Flammini, and
Menczer 2021; Jiang et al. 2021), but their role in exposure misinformation,
especially in the context of the pandemic, has not yet been explored.

Previous works have identified a link between partisanship and misinformation:
politically conservative social media users are more likely to spread
misinformation (Grinberg et al. 2019; Nikolov, Flammini, and Menczer 2021) and
anti-science content (Rao et al. 2021). This link partly explains the
opposition by conservatives to Covid-19 mitigation measures (Gollwitzer et al.
2020). However, the interaction between partisanship and exposure to
misinformation has not been as well studied. As a result, we do not know
whether partisanship drives selective exposure to misinformation or people
selectively share misinformation despite being exposed to diverse and high
quality information sources.

In the context of a global pandemic and the public’s increasing reliance on
online information, it is important to understand the factors shaping public’s
exposure to polarized information and misinformation. We organize our research
around these questions:

RQ1

Is polarization of information people share (along partisan and factual
dimensions) aligned with the polarization of information they see within their
social networks? Are there multidimensional echo chambers?

RQ2

How well are the dimensions of polarization correlated, i.e., how much is
partisanship correlated with content factuality?

RQ3

Are exposures to misinformation asymmetrical along partisan lines?

RQ4

Are there partisan asymmetries in the selective amplification or filtering of
misinformation?

RQ5

Do people pay more attention to factual content or misinformation?

Our study addresses this challenge by examining online discussions about the
Covid-19 pandemic. First, we classify social media users ideologically along
political and factual dimensions, assigning them a multi- dimensional
polarization score. Next, we quantify multi-dimensional polarization of the
information users see in their friends’ posts. As a proxy of friends, we take
the messages posted by accounts the user retweets. We identify echo chambers
that expose users to ideologically congruent information along political and
factual dimensions. While social media users tend to surround themselves with
peers who share similar views on politics and factuality, there are partisan
asymmetries in exposure to factual content. Additionally, the substantial
interaction between the two dimensions, also observed in earlier studies
(Grinberg et al. 2019), creates conditions for politically polarized users to
amplify misinformation. These polarized users, who represent hardline
partisans on both sides of the political spectrum, selectively share
misinformation. However, such users receive less attention than those sharing
factual content, and political moderates, who represent the bulk of users in
our study, help filter out misinformation, reducing the amount of low-
factuality content in the information ecosystem. Identifying the extent of
polarization and how political ideology can exacerbate misinformation can
potentially help public health experts and policy makers improve their
messaging to facilitate consensus and compliance with public health measures.

## Related Work

Researchers define polarization as divergence of opinions along the political
dimension and study its impact on other opinions, such as scientific topics
(Bessi et al. 2016). However, opinions on controversial topics are often
correlated (Baumann et al. 2020). For example, those who oppose lockdowns as a
way to suppress the spread of the disease also resist vaccinations. To capture
some of the complexity of polarization we project opinions in a multi-
dimensional space, with different axes corresponding to different semantic
dimensions. Once we identify the dimensions of polarization and measure them,
we can study dynamics of polarized opinions, their interactions and regional
differences.

Label propagation leverages structure of connections in a network to infer the
political views based on the ideology of the accounts users retweet (cf.
(Badawy, Ferrara, and Lerman 2018)). The intuition behind the approach is that
people prefer to connect to (e.g., retweet content posted by) others who share
their opinions and ideology (boyd, Golder, and Lotan 2010; Metaxas et al.
2015).

Others have looked at echo chambers in the context of online discourse. The
authors in (Cinelli et al. 2021) studied the effect of echo chambers across
different platforms, finding that Facebook is more segregated than other
platforms. Interestingly, they find that those platforms that allow users to
adjust their feed (e.g., Reddit) afford more balanced media consumption than
those that don’t (e.g., Twitter and Facebook). Our work builds on this by
providing a multidimensional understanding of the exposure of particular users
to certain content. In another work, (Nikolov, Flammini, and Menczer 2021)
finds that misinformation sharing is strongly correlated with right leaning by
studying the network and content. This finding is echoed in (Rao et al. 2021).
This is an important departure from our work. We find that left leaning folks
also produce content with low factuality. (Rao et al. 2021) also finds that
the number of moderate, factual users greatly surpasses the number of
misinformants. We see this in our data as well, with more factual users as
demonstrated in Fig. 1(b).

## Methods

### Data

In this study, we use the publicly available dataset (Chen, Lerman, and
Ferrara 2020) comprising of 260.6M tweets related to Covid-19 posted between
January 21 and July 31, 2020. These tweets contain at least one of a
predetermined set of Covid-19-related keywords (e.g., coronavirus, pandemic,
Wuhan, etc.). However, less than 1% of the tweets have geographic coordinates
associated with them. We therefore rely on the geolocation
method111https://github.com/julie-jiang/twitter-locations-us-state employed in
(Jiang et al. 2020) to determine if the user is within the US. The method
works by first extracting the mentions of city or state users frequently have
in their profile before employing a fuzzy matching algorithm to match them to
their respective states in the US. A manual review of this approach found it
to be effective in identifying user’s home state. This leaves us with 48M
tweets generated by 2.4M geolocated users in the United States.

### Dimensions of Polarization

In this study, we characterize attitudes along two dimensions: political and
factual. The political dimension captures user political ideology or
partisanship, ranging from hardline liberal to hardline conservative, while
the factual dimension quantifies user’s predilection for factual content or
misinformation. With Media Bias-Fact Check222http://mediabiasfactcheck.com
providing an exhaustive list of domains and their ideological polarities,
previous studies have leveraged users’ domain sharing behaviors on Twitter
(Cinelli et al. 2021; Le et al. 2019; Rao et al. 2021) to quantify ideological
alignment. Along the political scale, Media Bias-Fact Check lists over 2K pay-
level domains (PLDs) under five mutually exclusive categories: Left, Center-
Left, Least-Biased/Center, Center-Right and Right. In addition, it also
provides a measure of reporting quality for over 3.5K pay-level domains
belonging to one of the six content factuality classes: Very Low, Low, Mixed,
Mostly Factual, High and Very High. Sources generating pro-science content are
categorized as High or Very High while, sources propagating misinformative,
questionable content or anti-science content are categorized as Low or Very
Low on the factuality scale. On the other hand, highly partisan news sources
such as foxnews.com, cnn.com, huffpost.com generally have a chequered quality
of reporting and have been listed as Mixed. Table 1 refers to the collection
of information sources and their ideological biases.

Dimension |  Polarity |  Pay-Level Domains
---|---|---
Politics |  Left $(0)$ |  cnn.com, huffpost.com, dailybeast.com,$\ldots$ (350+ PLD s)
Center-Left $(0.25)$ |  aljazeera.com, independent.co.uk, lincolnproject.us $\ldots$ (500+ PLDs)
Center $(0.5)$ |  gallup.com, pewresearch.co.uk, wikipedia.com $\ldots$ (500+ PLDs)
Center-Right $(0.75)$ |  bostonherald.com, chicagotribune.com, wsj.com $\ldots$ (250+ PLDs)
Right $(1)$ |  foxnews.com, gppusa.com, thenationalherald.com $\ldots$ (250+ PLDs)
Factuality |  Very Low $(0)$ |  counterthink.com, biggovernment.news, vaccines.news $\ldots$ (180+ PLDs)
Low $(0.2)$ |  911truth.org, althealth-works.com, naturalcures.com $\ldots$ (600+ PLDs)
Mixed $(0.4)$ |  breitbart.com, buzzfeed.com, independent.co.uk $\ldots$ (1000+ PLDs)
Mostly Factual $(0.6)$ |  drudgereport.com, washingtonpost.com, bloomberg.com $\ldots$ (200+ PLDs)
High $(0.8)$ |  azcentral.com, bbc.com, nbcnews.com $\ldots$ (1300+ PLDs)
Very High $(1)$ |  nationalacademyofsciences.org, nature.com, bmj.com $\ldots$ (200+ PLDs)

Table 1: Curated pay-level domains and their polarity scores along political
and factual dimensions. For the political dimension, $\\{0,0.25,0.5,0.75,1\\}$
represents Left, Center Left, Center/Unbiased, Center Right and Right sources
respectively. Along the factuality or misinformation dimension, Very Low, Low,
Mixed, Mostly Factual, High and Very High are quantified as
$\\{0,0.2,0.4,0.6,0.8,1\\}$ in the same order

.

### Measuring Polarization

We measure ideological polarization by looking at the political and factual
scores of domains people share in their posts and see their friends share.

#### Individual Polarization (Information Sharing)

We extract tweets containing URLs and use tldextract
333https://pypi.org/project/tldextract/ to extract pay-level domains from
them. We filter out tweets and retweets containing pay-level domains that are
not categorized under either of the two ideological polarities of interest
(Table 1).

(a) Political Leaning Score

(b) Factual Leaning Score

Figure 1: (a) Distribution of Political Leaning domain scores. (b)
Distribution of Factual Leaning domain scores.

Similar to previous works (Cinelli et al. 2021; Rao et al. 2021), we infer a
user’s partisanship by averaging over the political scores of the PLDs the
user shared. Likewise, we infer individual’s factual scores by averaging the
factual scores of the PLDs the user shared. This makes our measure of factual
sharing similar to the propensity, or vulnerability, to misinformation used in
previous works (Grinberg et al. 2019; Nikolov, Flammini, and Menczer 2021). It
is important to note that individual scores quantify the information that
users generate within the online information ecosystem; therefore, users with
low factual scores produce more misinformation.

We calculate user $u$’s sharing behaviors along the political $p_{l}(u)$ and
factual $f_{l}(u)$ dimensions using Eqs 1 and 2 respectively. We denote the
set of pay-level domains shared by user $u$ as $D(u)$. These include only the
domains appearing in $u$’s original tweets. Functions $\Pi(d)$ and $\Phi(d)$
return the political and factual polarity of each domain $d$.

$p_{l}(u)=\frac{1}{|D(u)|}\sum_{d\in D(u)}\Pi(d)$ (1)

$f_{l}(u)=\frac{1}{|D(u)|}\sum_{d\in D(u)}\Phi(d)$ (2)

Fig. 1 shows the distribution of user leanings along the political and factual
dimensions. The political distribution is skewed to liberal domains,
potentially indicating a bias in the Covid-19 data. Similarly, individual
factual leanings are skewed towards factuality, and there are relatively few
users sharing misinformation or low-factuality content.

#### Neighborhood Polarization (Exposure)

Understanding the polarization of information people see online is challenging
for several reasons. On Twitter, as on other social media platforms, users
subscribe to follow accounts of other users to see the content they post.
However, the follower graph is usually not available nor is it feasible to
reconstruct it from the available APIs. Even when the follower graph is known,
platform’s personalization algorithms may select only a subset of the messages
posted by friends, i.e., the accounts the user follows, in the user’s timeline
(Bakshy, Messing, and Adamic 2015). This can dramatically change not just the
number but also the nature of the information people see (Bartley et al. 2021;
Chen et al. 2020).

As a proxy of the follower graph, we use the retweet graph, creating links to
the accounts the user retweets. We consider the retweeted accounts as friends
whose activity the user sees. We extract tweets and retweets generated by
friends, extract PLDs and filter out ones that do not have a political or
factual polarity. In contrast to previous works (Cinelli et al. 2021;
Garimella et al. 2018; Nikolov, Flammini, and Menczer 2021), however, which
measure polarization of a user’s neighborhood by averaging over friends’
individual political leanings, we aggregate over all messages posted by
friends and calculate political and factual scores of aggregated tweets. This
approach factors in the large variation in friend activity: an active friend
who posts many messages will have a bigger effect on the user’s information
exposure than a less active friend.

Information exposure scores along political ($p_{e}(u)$) and factual
($f_{e}(u)$) dimensions are calculated using Equations 1 and 2, but now the
set of pay-level domains $D(u)$ corresponds to all domains user $u$ sees,
which we construct by aggregating over all PLDs shared by $u$’s friends.

Figure 2: Distribution of number of PLDs users generate (shown in blue) and
are exposed to (shown in orange).

After filtering out users who share or see two or fewer PLDs with political
and factual polarity, we are left with a little over 350K users. Fig. 2 shows
the distribution of the number of pay-level domains users share in their
posts, as well as the distribution of the number of PLDs users see. The
difference between the two distributions suggests that some domains are seen
much more than they are shared, likely because they are shared by influential
accounts with many followers. In this paper we study the relationship between
the polarization of information people see on social media and polarization of
information they themselves share.

## Results

(a) Political vs Political Leaning (b) Factual Exposure vs Factual Leaning

Figure 3: Ideological echo chambers. Heatmap of (a) political leaning and
political exposure ($r=0.61^{***}$) and (b) factual leaning and factual
exposure ($r=0.50^{***}$). Colors indicate the number of users.

We now explore the relationship between individual polarization of users
discussing Covid-19 online and the polarization of information to which they
are exposed.

### Existence of Political and Factual Echo-Chambers

First, we explore the relationship between individual polarization and
polarization of information exposure separately along each dimension. Fig. 3
shows the joint distribution of individual political (Fig. 3(a)) and factual
(Fig. 3(b)) leanings and the political (resp. factual) scores of the
information they are exposed to by friends in their retweet neighborhood. The
high density along the diagonal confirms the existence of echo chambers: many
users are linked to friends who expose them to ideologically congruent
information. The correlation between individual leanings and information
exposure scores along the political and factual dimensions are $0.61\
(p<0.001)$ and $0.5\ (p<0.001)$ respectively. There are no partisan
asymmetries in the political echo chambers (Fig. 3(a)), as both liberal and
conservative users are exposed to a similar variety of political content.
There is some asymmetry in factual information echo chambers (Fig 3(b)), since
there is much lower density of users in the misinformation bubble. Unlike
previous works, e.g., (Cinelli et al. 2021), the echo chambers we observe are
more diffuse, with users linked to friends with more variable ideologies. This
is because previous works calculate the average polarization of friends, which
gives equal weight to friends who share a lot or little information, while we
aggregate in- formation shared by all friends when measuring polarization.

### Polarization is Multi-dimensional

Figure 4: Relationship between individual political and factual leanings.
Color represents number of users. Pearson’s correlation $r=-0.198\ (p<0.001)$.

Previous research has shown that political and factual dimensions of
information people share online are correlated: conservatives share
misinformation to a greater degree than liberals (Vosoughi, Roy, and Aral
2018; Grinberg et al. 2019; Nikolov, Flammini, and Menczer 2021), and they
also tend to share more anti-science sources (Rao et al. 2021). Our results
are consistent with these findings. Fig. 4 shows the distribution of user
scores in the political-factual space. There is a strong negative correlation
($-0.198,\ p<0.001$) between user leanings along the two dimensions: users
sharing more conservative domains are more likely to share misinformation.
However, the large variance masks more nuanced positions. For example, the
bright line in the upper-left quadrant shows a phenomenon also observed by
(Nikolov, Flammini, and Menczer 2021) that more extreme liberals have a
greater propensity to share misinformation.

(a) Factual topics (b) Misinformation topics

Figure 5: Word cloud of popular hashtags frequently used by (a) factual users
($f_{p}\geq 0.6$) and (b) users with high propensity for misinformation
($f_{p}\leq 0.4$).

.

Fig. 5(b) contrasts popular topics (hashtags) discussed by people sharing
factual information and misinformation. While factual people post messages on
health topics, such as “pandemic”, “wearamask”, “stayhome”, people sharing
misinformation are preoccupied with politics (“trump2020”, “kag2020”,
“democrats”, “maga”), conspiracies (“plandemic”, “qanon”, “wwg1wga”).
Interestingly, these users also mention media to a much greater extent, using
topics like “foxnews”, “7news”, “foxandfriends”, “morningjoe”, and “fakenews”.
This may suggest the greater role that media plays in agenda-setting for
people vulnerable to misinformation. Also, unlike factual users, people
spreading misinformation also discuss unproven cures, like
“hydroxychloroquine”.

### Partisan Asymmetries in Exposure to Misinformation

Information exposure within multi-dimensional echo chambers
---
|
(a) Political Exposure $p_{e}$ vs Political Sharing $p_{l}$ | (b) Factual Exposure $f_{e}$ vs Factual Sharing $f_{l}$
Individual polarization within multi-dimensional echo chambers
|
(c) Political Exposure $p_{e}$ vs Political Sharing $p_{l}$ | (d) Factual Exposure $f_{e}$ vs Factual Sharing $f_{l}$
Figure 6: Multi-dimensional polarization and information exposure within echo
chambers. (a) Color indicates the median factual leaning score in each bin. In
general, as users produce more conservative content while being exposed to
more conservative content, they have a higher propensity to misinformation.
Low content factuality is also seen along the liberal extreme where users
produce far-left content. (b) Color indicates the median political leaning
score in each bin. Generally, as users generate more misinformation while
being exposed to low factual content, they have a higher propensity to
political conservatism.

How does the interaction between partisanship and factuality affect what
information the users are exposed to within their echo chambers and, in turn,
what information they share? Do people effectively filter out misinformation
they see by selectively sharing more factual content? Or do they amplify
misinformation by selectively sharing fewer factual domains than what they are
exposed to?

Fig. 6 visualizes user exposure to multi-dimensional information within the
echo chambers. The top row shows user exposure to political and factual
information as a function of user political (Fig. 6(a)) and factual (Fig.
6(b)) leanings. Note the neighborhood exposure vs leaning space is the same
shown in Fig. 3(a) and (b) for respectively. The color in each plot shows the
median exposure score. There are several regions of interest in Fig. 6(a).
Liberal users ($p_{l}<0.5$) who are exposed to politically moderate content
($p_{e}\approx 0.5$) see the most factual information (dark orange). Liberals
($p_{l}<0.5$) who are exposed to liberal content ($p_{e}<0.5$) generally see
more factual (orange) information, although as their exposure becomes more
partisan, the share of misinformation they see grows. Those exposed to extreme
left content ($p_{e}\approx 0$) see more misinformation (green hue). As
liberals become more exposed to conservative content ($p_{e}\rightarrow 1$)
they see more and more misinformation The same is not true of conservatives:
conservative users ($p_{l}>0.5$) who are exposed to right-wing information
($p_{e}>0.5$) tend to see more misinformation; however, as long as they are
not too conservative, exposure to liberal information ($p_{e}<0.5$) allows
them to receive more factual information. Unlike liberals, exposure to
politically moderate content ($p_{e}\approx 0.5$) does not promote factual
information among conservatives.

Trends within misinformation echo chambers (Fig. 6(b)) tell a similar story.
Users who generate more misinformation ($f_{l}<0.4$) and are exposed to
misinformation ($f_{e}<0.4$) tend to see more conservative content (red),
although those who are exposed to more factual content ($f_{e}\rightarrow 1$)
see more liberal information (blue dots). Among people sharing factual
information ($f_{l}>0.6$), those who are exposed to more factual information
($f_{e}\rightarrow 1$) tend to see politically moderate content (white). The
box outline is an artifact of domain polarity scores. MBFC classifies many
information sources as “mixed” (0.4), leading to an overabundance of points
near that value.

The bottom row of Fig. 6 visualizes multi-dimensional polarization within the
echo chambers. Again, the neighborhood exposure vs leaning space is the same
as the row above, but the color in each plot shows user polarization or
leaning along the alternate dimensions. Fig. 6(c) shows that as partisanship
becomes more extreme ($p_{l}\rightarrow 0$ or $p_{l}\rightarrow 1$), people
are more likely to share misinformation (green). Interestingly, this trend
does not strongly depend on partisanship of their exposure ($p_{e}$). Overall,
liberals ($p_{l}<0.5$) share more factual information, although those who are
more moderate ($p_{l}\approx 0.5$) tend to share more misinformation
(yellow/green) as they are exposed to more conservative content
($p_{e}\rightarrow 1$). As shown in Fig. 6(d), misinformation-prone users
($f_{l}<0.4$) tend to post more hardline conservative content (darker red) as
they share more misinformation ($f_{l}\rightarrow 0$) regardless of their
exposure; however, those who are most exposed to misinformation ($f_{e}<0.2$)
tend to share more liberal views (blue dots). This is not true for factual
users, who tend to share liberal content (blue) regardless of the factuality
of their exposure ($f_{e}$).

### Hardline Partisans Amplify Misinformation

The off-diagonal elements in the echo chamber plots in Fig. 3 suggest that a
sizable fraction of social media users share information that is more
polarized and less factual than what they are exposed to, and an equally large
number share information that is more factual than what they are exposed to.
In other words, some people filter out misinformation from the information
ecosystem, while others amplify it. To better understand how the patterns in
the interaction between political and factual dimensions affect how people
react to exposure, we define two quantities.

$\displaystyle\Delta_{f}(u)$ $\displaystyle=$ $\displaystyle
f_{l}(u)-f_{e}(u)$ (3) $\displaystyle\Delta_{p}(u)$ $\displaystyle=$
$\displaystyle|p_{l}(u)-0.5|-|p_{e}(u)-0.5|$ (4)

Equation 4 simply gives excess factuality (amplified factuality) for a given
user, i.e., how much more factual content the user shares relative to their
exposure. Equation 4 measures excess partisanship (amplified partisanship),
i.e., the relative partisanship of the content the user shares compared to
their exposure. Note that we had transformed polarization scores so that
instead of partisanship, they measure the degree of political moderacy or
extremism regardless of its ideological label.

Figure 7: Excess factuality $\Delta_{f}$ vs Excess partisanship $\Delta_{p}$ Figure 8: Boxplots comparing activity and power of factual ($f_{l}\geq 0.6$) and misinformative users ($f_{l}\leq 0.4$). While we notice that misinformative users are more active both in terms of number of tweets and retweets generated, they are retweeted less com- pared to factual users. Subsequently, the ratio of retweets received to overall activity is significantly lower for misinformative users than factual ones. Metric $(\theta)$ |  Factual ($\mu(\theta)_{F}$) |  Misinformation ($\mu(\theta)_{M}$) |  Hypotheses |  t-statistic
---|---|---|---|---
$T$ |  $38.93$ |  57.01 |  $H_{0}:\mu(log(T))_{F}=\mu(T)_{log(M)}$ |  $\textbf{16.75}^{***}$
$H_{a}:\mu(log(T))_{F}\neq\mu(log(T))_{M}$
$RT$ |  $120.69$ |  168.28 |  $H_{0}:\mu(log(RT))_{M}\leq\mu(log(RT))_{F}$ |  $\textbf{27.42}^{***}$
$H_{a}:\mu(log(RT))_{M}>\mu(log(RT))_{F}$
$A=T+RT$ |  $159.63$ |  225.29 |  $H_{0}:\mu(log(A))_{M}\leq\mu(log(A))_{F}$ |  $\textbf{28.89}^{***}$
$H_{a}:\mu(log(A))_{M}>\mu(log(A))_{F}$
$R$ |  61.97 |  $48.82$ |  $H_{0}:\mu(log(R))_{F}\leq\mu(log(R))_{M}$ |  $\textbf{25.64}^{***}$
$H_{a}:\mu(log(R))_{F}>\mu(log(R))_{M}$
$P=R/A$ |  0.57 |  $0.17$ |  $H_{0}:\mu(log(P))_{F}\leq\mu(log(P))_{M}$ |  $\textbf{32.86}^{***}$
$H_{a}:\mu(log(P))_{F}>\mu(log(P))_{M}$
Table 2: Results of hypothesis testing for difference in means between the two
groups of users along the factuality dimension for various metrics. Factual
users $(F)$ have high factuality scores ($f_{l}\geq 0.6$) while misinformation
users $(M)$ have low scores ($f_{l}\leq 0.4$). Metrics include: number of
tweets $(T)$ and retweets $(RT)$ generated by the user, the overall activity
$(A)$, number times the user is retweeted $(R)$ and retweet power $(P)$ which
is the ratio of number of times retweeted and activity. We performed $t$-tests
to assess the statistical significance of difference between the two
distributions after log transforming the variables. ∗∗∗ denotes a
statistically significant difference between the means of the two
distributions with p-value $<0.001$.

Fig.7 shows the joint distribution of excess partisanship $\Delta_{p}$ and
excess factuality $\Delta_{f}$. The negative correlation (Pearson’s
correlation $r=-0.38,$ $p<0.001$) between the two dimensions suggests that not
only do politically hardline social media users (regardless of whether they
are liberal or conservative) have a higher propensity for misinformation, but
users who amplify politically polarized content also amplify misinformation.
The color shows partisanship. Interestingly, both hardline conservatives and
hardline liberals are active in amplifying partisanship $\Delta_{p}>0$ and
misinformation $\Delta_{f}<0$, with liberals playing a more active role in
amplifying misinformation (dampening factuality). On the other hand, users who
are less partisan than their neighborhood ($\Delta_{p}<0$) also share more
factual information than what they are exposed to ($\Delta_{f}>0$). By
filtering out misinformation, such users play an important role in the
information ecosystem. They also tend to be politically moderate.

### Less Attention to Misinformation

The presence of users generating low factuality content raises questions about
their activity and the subsequent attention they garner. Are users sharing
misinformation more active than users sharing more factual content? Does
aggressive content generation correlate with more attention? These are some of
the questions that become imperative to assess the ravages of misinformation
on social media.

We define a user’s overall activity as the sum of the tweets $T$ and retweets
$RT$ they generate $A(u)=T(u)+RT(u)$. In order to quantify the attention the
user $u$ receives in response to their activity, we define retweet power
$P(u)$ as the ratio of number of times $u$ is retweeted $R$ and their overall
activity:

$P(u)=\frac{R(u)}{A(u)}=\frac{R(u)}{[T(u)+RT(u)]}$ (5)

Boxplots in Fig. 8 visualize the differences in tweet and retweet activity of
factual $(f_{l}\geq 0.6)$ and misinformation $(f_{l}\leq 0.4)$ users. To
assess the significance of differences between the two groups, we use the
Student’s $t$-test. This parametric test of difference between the means of
two groups requires the corresponding distributions to be normal. While our
metrics (the number of tweets and retweets) have a skewed distribution, taking
a log transform increases normality. Table 2 details the null and alternate
hypotheses used in our $t$-tests.

From Fig.8 and Table 2, we see that misinformative users tweet and retweet
more often and have higher overall activity compared to factual users.
Statistically significant $t$-statistics for $T$,$RT$ and $A$ in Table 2
reinforce these findings.

Despite their increased overall activity, users sharing misinformation are
retweeted less often than factual users $(\mu(R_{M})<\mu(R_{F}))$, significant
at $p<0.001$ and have considerably lower retweet power
$(\mu(P_{M})<\mu(P_{F}))$ at $p<0.001$ (Fig.8(d)) . These findings hint at an
increased attention to factual users despite their lower overall activity.

## Conclusion

In this work we study multi-dimensional polarization and echo chambers. We
focus on two dimensions of polarization—political and factual—which are
assessed by measuring the bias in the pay-level domains that the users tweet.
We find that there is strong polarization along both dimensions. To deepen the
understanding of the mechanics behind echo chambers, we separate a user’s
interactions into their exposure (what their friends post) and leaning (what
they post). We find a strong correlation between what a user sees and what the
post, confirming the presence of echo chambers.

Next, we study the partisan asymmetries of these echo chambers. We find that
conservatives are more likely to share misinformation than liberals.
Nevertheless, extremely liberal partisanship increases a user’s propensity to
share misinformation. Moreover, we find that moderate liberals have the
highest exposure to factual information. We find that this does not hold for
conservatives whose exposure to liberal information yields the most truthful
information. Furthermore, for conservatives exposure to politically moderate
content does not make them more factual.

Lastly, we look at the relationship between partisan extremism and
misinformation. We find that highly polarized users, who represent hardline
partisans on both sides of the political spectrum, are most likely to amplify
partisan content and misinformation. However, such users get less attention
than the bulk of users in our study who are political moderates who
selectively share more factual content. Therefore, such users filter out
misinformation.

There are several limitations to this study worth considering. First, we do
not know the actual exposures and thus rely on the retweet network as a proxy.
Second, there could be factual/pro-science bias in the data due to the way it
was collected. More generally, the keyword-based Twitter crawl used to produce
this data could omit nuanced subtopics related to Covid-19 discussions.
Lastly, our study focuses on users in the United States. This decision was
made because of the United States’ information environment, and due to the
dominance of English keywords used to collect the dataset in our study.

This work identifies important differences in the information space of
polarized and partisan users. Better understanding how information is
received, and how it propagates, can help public health experts craft more
effective messaging. There are several important avenues for future work, such
as designing effective interventions for misinformation, assessing the
relationship between partisan asymmetries and the binding dimensions of moral
thinking such as loyalty, authority and purity, and studying the temporal
dynamics of these echo chambers.

### Acknowledgments

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