# Authority without Care:
Moral Values behind the Mask Mandate Response

Yelena Mejova,1 Kyriaki Kalimeri,1 Gianmarco De Francisci Morales2

###### Abstract

Face masks are one of the cheapest and most effective non-pharmaceutical
interventions available against airborne diseases such as COVID-19.
Unfortunately, they have been met with resistance by a substantial fraction of
the populace, especially in the U.S. In this study, we uncover the latent
moral values that underpin the response to the mask mandate, and paint them
against the country’s political backdrop. We monitor the discussion about
masks on Twitter, which involves almost 600k users in a time span of 7 months.
By using a combination of graph mining, natural language processing, topic
modeling, content analysis, and time series analysis, we characterize the
responses to the mask mandate of both those in favor and against them. We base
our analysis on the theoretical frameworks of Moral Foundation Theory and
Hofstede’s cultural dimensions.

Our results show that, while the anti-mask stance is associated with a
conservative political leaning, the moral values expressed by its adherents
diverge from the ones typically used by conservatives. In particular, the
expected emphasis on the values of authority and purity is accompanied by an
atypical dearth of in-group loyalty. We find that after the mandate, both pro-
and anti-mask sides decrease their emphasis on care about others, and increase
their attention on authority and fairness, further politicizing the issue. In
addition, the mask mandate reverses the expression of Individualism-
Collectivism between the two sides, with an increase of individualism in the
anti-mask narrative, and a decrease in the pro-mask one. We argue that
monitoring the dynamics of moral positioning is crucial for designing
effective public health campaigns that are sensitive to the underlying values
of the target audience.

## Introduction

The COVID-19 pandemic has been an unprecedented event, which has also brought
about an infodemic that makes public health response difficult. COVID denial,
anti-vaccine sentiment, and other flavors of theories (from doubts to full-
blown conspiracies) have been documented in social media (Patwa et al. 2021).
Among the many controversies, surprisingly, wearing a mask has become
extremely politicized and contentious. Inconsistent messaging by public health
organizations has seriously undermined public compliance to this simple
measure. Initially, the WHO recommended that masks should be worn only by
professionals or those taking care of a sick person (WHO 2020), and in the
U.S., the Center for Disease Control and Prevention (CDC) recommended the use
of masks only for healthcare workers and people who were sick. Only on April
3rd, 2020, the CDC officially recommended wearing non-medical cloth face
coverings when in public places (Landsverk 2020). Soon after, speculations
that masks do more harm than good (Gillespie 2020) and that they foster a
false sense of security (Khan 2020) started to proliferate. Public health
experts have acknowledged that it has been a challenge to communicate with
groups holding strong values of freedom (Hartsoe 2020). Indeed, globally,
research has found that the value of individualism is associated with higher
COVID-19 mortality rates (Maaravi et al. 2021) and less adherence to
preventive measures (Biddlestone, Green, and Douglas 2020). However, how
personal moral values evolve around governmental messaging concerning these
preventive measures is not yet understood.

Mask-wearing behavior and attitudes can be influenced by multiple factors.
There is extensive evidence that political ideology and identity influences
attitudes, judgments, and behaviors (Van Bavel and Pereira 2018). At the same
time, political identity is deeply intertwined with moral values (Graham,
Haidt, and Nosek 2009; Hatemi, Crabtree, and Smith 2019). Finally, and perhaps
obviously, moral values are directly linked to moral decision making
(Karandikar et al. 2019), which completes the triad. In particular, there is
an altruistic component in mask-wearing, as their main purpose is to protect
others, which is related to solidarity towards the in-group in the face of an
out-group threat (Campbell 1965). For this reason, among moral values we pay
particular attention to the individualist-collectivist angle. Finally, there
is ample literature on the division and polarization of news and information
sources in general.111https://www.pewresearch.org/journalism/2020/03/04/about-
one-fifth-of-democrats-and-republicans-get-political-news-in-a-kind-of-media-
bubble As users increasingly rely on social media to satisfy their information
needs,222https://www.pewresearch.org/journalism/2021/11/15/news-on-twitter-
consumed-by-most-users-and-trusted-by-many the lines between professional
reporting and personal opinions begin to blur. For this reason, we
contextualize the information environment in which the mask debate takes place
in terms of information sources from peers—within the Twitter community—and
from external sources.

This work provides a fine-grained analysis of the moral values of those
expressing opinions around masking by applying the Moral Foundations Theory to
a dataset of Twitter posts spanning the beginning of the pandemic, from
January to July 2020. In particular, we ask: _What is the anatomy of the
collective discussion on mask wearing around the mask mandate on Twitter?_ In
particular, we analyze different facets of this discussion in the U.S.:

  * 1.

How does the users’ stance relate to their political leaning?

  * 2.

What moral values do adherents to pro- or anti-masking stances hold?

  * 3.

What is the information environment around their arguments?

Our findings confirm the known political divisions, with liberals supporting
the pro-mask and conservatives the anti-mask stance. The moral narrative of
the two sides also differs: those on the anti-mask side invoke the values of
_authority_ and _purity_ , while those promoting mask-wearing emphasize _care_
and _loyalty_. The introduction of the mask mandate shifts these values from
an emphasis on _care_ to an increased attention on _authority_ and _fairness_
, thus escalating the politicization of the issue. Content analysis shows that
the increasingly individualistic views espoused by the anti-mask side are
accompanied by the allegations of the dangers and ineffectiveness of mask-
wearing, supported by resources from social media and sometimes even
governmental and scientific sources. We argue that the mask-wearing debate has
a dynamic moral landscape that should be carefully monitored to design
effective public messaging campaigns that reflect the shifting values of their
target audience. Such studies are especially important, given the rising
prominence of social media as a platform for public policy discussion and
influence (Shapiro and Hemphill 2017).

## Related Work

#### Masking Behavior.

Mask mandates have been shown to be effective in decreasing COVID infection
rates in the U.S., both in urbanized and rural areas (Krishnamachari et al.
2021). Early studies have tested the effect of mask adoption, with simulations
showing that even relatively ineffective face coverings could meaningfully
reduce community transmission (Eikenberry et al. 2020). Unfortunately, the
public response to the masking mandates has been partial, with only a 12%
increase in mask-wearing immediately after the CDC masking guideline on April
3, 2020 (Goldberg et al. 2020). A survey of residents of 10 states in the U.S.
relates mask-wearing to COVID cases in the state, political party in power,
and individual measures of “social capital”, as well as some demographics
(Hao, Shao, and Huang 2021). They find that respondents are more likely to
wear a mask if there are more COVID-related deaths in the state (Odds Ratio of
$1.26$), the Democratic party is in power (OR $=2.0$), and if the respondents
more often speak to their friends and family (OR $=1.16$). Although
misinformation has been blamed for the resistance to mask-wearing and social
distancing, a nationally representative survey has found that the beliefs
about the consequences of these behaviors are more predictive of people’s
compliance (Hornik et al. 2021). Especially in the U.S., beliefs and trust in
authority are often strongly related to the people’s political affiliations.
For instance, Republicans, conservatives, and nationalists are less likely to
believe that the World Health Organization (WHO) can effectively manage the
pandemic (Bayram and Shields 2021).

#### Social Media & Masking.

A study of tweets during the early days of the COVID pandemic (Feb-March 2020)
identified methods for decreasing the spread of COVID as one of the main
themes and the wearing of masks as one of those most associated with positive
sentiment (Abd-Alrazaq et al. 2020). However, authors of a later study
spanning January to October 2020 showed that the output of automated sentiment
analysis tools corresponds poorly with the mask-related sentiment expressed in
the tweets due to the richness of the language used (He et al. 2021). Instead,
they perform manual coding of a sample of the tweets to identify several major
categories of concerns around masking, including physical discomfort,
effectiveness, appropriateness, and political beliefs. A network analysis of
the mask-related tweets has shown the pro-mask activists exist in a kind of
“echo chamber” (Cinelli et al. 2021; Garimella et al. 2018a), and that they
tend to ignore the subversive rhetoric of the anti-mask fringe (Lang,
Erickson, and Jing-Schmidt 2021). Instead, a recent Twitter study found a
focus on ongoing news (Cotfas et al. 2021). This fringe is more likely to use
toxic language, including insults and profanity, than the pro-mask ones
(Pascual-Ferrá et al. 2021). The authors link this behavior to either the
vociferous protestations of a minority group (Miller and Morrison 2009) or
potential signaling as an in-group behavior and a marker of personal
identification. Alongside this toxicity, other studies find widespread
misinformation and misunderstandings in the social media discussions around
mask use. These include the beliefs that COVID19 is over-hyped by the media,
that masks are ineffective, and that they do more harm than good (Keller,
Honea, and Ollivant 2021). These beliefs were then shown to impact the mask-
wearing (and social distancing) behaviors (Hornik et al. 2021). In this study,
we introduce a dimension of _moral values_ , which we argue underlies some of
the disagreements on the appropriate use of masks during the epidemic, and
which may shed some light on the moral reasoning behind the rhetoric.

#### Theoretical Framework.

Our study of the rhetoric around masks during COVID-19 is grounded in the
Moral Foundations Theory (MFT), its manifestation in interpersonal and inter-
group communication, and its reflection at the societal level as an
individualist or collectivist cultural dimension. According to social identity
theory, members of an in-group will look for negative aspects of an out-group,
thus enhancing their self-image (Tajfel et al. 1979). Strong in-group and out-
group reasoning at a societal level determines whether a society is
individualistic or collectivist (IC), as defined by Hofstede (2001). The IC
dimension considers the degree to which societies are integrated into groups
and their perceived obligations and dependence on groups. Individualism
indicates there is a greater importance placed on attaining personal goals.
Collectivism indicates there is a greater importance placed on the goals and
well-being of the group. People in collectivist societies generally
distinguish sharply between in- and out-groups, while people in
individualistic societies treat everyone as a potential in-group member and
thus apply universal values to everyone. Cross-cultural research has
demonstrated that the United States is the prototypical individualist culture
based on the IC dimension (Hofstede 1984; Kim et al. 1994). At the same time,
the U.S. show a measurable variation on this dimension (Vandello and Cohen
1999) at state level.

The societal dimensions of collectivism and individualism can be related to
the individuals’ adherence to specific moral dimensions, as postulated by the
Moral Foundations Theory (Graham et al. 2012; Samuel et al. 2016), which
include: _care/harm_ , fundamental concerns for the suffering of others,
including virtues of caring and compassion; _fairness/cheating_ , concerns
about unfair treatment, inequality, and more abstract notions of justice;
_loyalty/betrayal_ , concerns related to obligations of group membership;
_authority/subversion_ , concerns related to social order and the obligations
of hierarchical relationships such as obedience, respect, and proper role
fulfillment; and _purity/degradation_ , with concerns about physical and
spiritual contagion, including virtues of chastity, wholesomeness, and control
of desires. These foundations are shown to underlie human judgements and
decision-making (Weber et al. 2015) on societal topics ranging from vaccine
hesitancy (Kalimeri et al. 2019; Beiró et al. 2023), to politics (Iyer et al.
2012), religion, and social cooperation (Haidt 2012; Curry 2016). Here, we
place the focal point on the linguistic analysis of values expressed via
Twitter, and aim to clarify peoples’ dispositions and attitudes towards
interpersonal and inter-group processes related to persuasion and
communication narratives.

The moral dimensions of MFT have also been linked to political ideologies (Day
et al. 2014; Kugler, Jost, and Noorbaloochi 2014), with conservatives
emphasizing the in-group relationships and tradition, and liberals endorsing
fairness and equal opportunity. In the United States, the mask-wearing measure
has also been strongly associated with the partisan divide. Social identity is
a primary reason behind people’s decision whether to wear a mask during the
pandemic (Powdthavee et al. 2021), and surveys show that faith in President
Trump is a strong predictor of refusal to social distance, and its effect is
largest among individuals high in binding foundations (Graham et al. 2020).
Indeed, the U.S. counties that showed strong support for Trump in 2016
practiced significantly lower mask-wearing in 2020 (Kahane 2021). This work
illustrates the strong connection between the attitudes expressed towards
mask-wearing on Twitter and the the political leaning of those expressing
them, and shows the shifts in moral emphasis after the mask mandates are
introduced.

## Data

We begin by collecting tweets mentioning the keywords “mask”, “facemask”,
“ffp3”, and “n95” (the latter two refer to popular kinds of masks), spanning
the dates of January 1st to July 30th, 2020, using the GOT3 library (Mottl
2021). These keywords were chosen by considering the special Twitter Covid-19,
stream333https://developer.twitter.com/en/docs/twitter-
api/tweets/covid-19-stream/filtering-rules and picking the most common English
keywords related to masks. This collection results in $18\,245\,298$ tweets
from $5\,935\,103$ users. Following recommendations from existing literature,
we then perform several filtering steps in order to ensure that the tweets can
be used to assess the stance of the user on masking, and that the account is
likely to belong to a human living in U.S.:

  1. 1.

Relevance classifier (details next).

  2. 2.

Exclude users whose location cannot be mapped to one of U.S. states or
Washington DC.

  3. 3.

Exclude those not having at least 1 English tweet (Garimella et al. 2016).

  4. 4.

Exclude users with only 1 tweet (Garimella et al. 2016).

  5. 5.

Exclude top 0.1% of users by the number of tweets (having higher posting rate)
(des Mesnards et al. 2022).

  6. 6.

Exclude users whose friends to followers ratio is $>$10 (Aggarwal,
Rajadesingan, and Kumaraguru 2012).

We proceed by making sure the tweets we collect are indeed about mask-wearing
due to the COVID-19 epidemic. Upon manual examination, we find several other
topics captured, such as advice on masking during protests, sports-related
wear, and beauty products. To remove such content, we use a set of _distant_
labels to identify the non-relevant content.444We use lists compiled by
finding top bigrams (considering the 300 most frequent) in the dataset and
then manually labeling them for non-relevance. The irrelevant list is {‘hair
mask’,‘gas mask’,‘sleeping mask’, ‘majora’s mask’,‘ski mask’,‘eye mask’,‘clay
mask’,‘tear gas’} and the relevant list is {‘covid’,‘coronavirus’,‘sars-
cov-2’,‘sars-cov2’,‘social distanc’,‘socialdistanc’}. We manually annotate a
balanced random selection of 300 tweets associated with a distant label and
find 96% accuracy in distinguishing between relevant and non-relevant tweets
(expected accuracy of a random baseline is 50%). We use $430\,568$ tweets with
the respective distant labels to train the relevance classifier. The model is
a logistic regression, trained on tf-idf-weighted unigram counts extracted
from the tweets and with inverse-proportional class weighting to mitigate
class imbalance. As text pre-processing, we remove URLs, numbers, handles,
hashtag (the # symbols), retweet indicators, and stopwords, keep whole words,
and perform snowball stemming. The accuracy in 5-fold cross-validation is
99.7%. We draw another balanced sample of 300 tweets thus classified and find
the accuracy of the classifier to be 82.3% (inter-annotator agreement overlap
of 90%, Cohen’s kappa of 0.76 between 3 annotators).

Next, we geolocate the tweets identified as relevant by mapping the user
location strings to the Geonames ID by using custom string matching,555Code
available at https://sites.google.com/site/yelenamejova/resources and to U.S.
zip codes by using the ‘uszipcode’
library.666https://pypi.org/project/uszipcode/ We apply basic pre-processing
(i.e., stopword and non-ASCII character removal). First, we filter locations
assigned to the U.S. territory by ‘uszipcode’ and then the remaining ones by
the Geonames library, aiming to recover the GPS coordinates of the smallest
possible administrative area. We are able to locate $1\,383\,729$ of the users
within a U.S. state or Washington DC. Our sample is representative of the U.S.
population distribution (2019 Census estimates) with a Pearson correlation of
$0.96$ ($n$=51), which suggests that there is little bias in the sampling of
the state in terms of number of users.

The latter four filtering steps aim at excluding users with either too little
engagement on the topic or those who post so much that they are likely to be
business or automated accounts. We do not use the popular tool Botometer, as a
recent study on mask-related tweets shows it to mostly find active human
Twitter users (He et al. 2021). At the end of this process, we are left with
$647\,730$ users. Finally, we use the Twitter API Friends call to collect the
information about whom these users follow (“followees” or “friends”), thus
resulting in the coverage of $598\,792$ users.

Table 1: Number of tweets and associated users at different stages of data collection. Dataset stage | N tweets | N users
---|---|---
Keyword-based collection | $18\,245\,298$ | $5\,935\,103$
Geo-location filter | $5\,685\,866$ | $1\,383\,729$
Engagement & network filter | $3\,614\,343$ | $598\,792$

## Results

#### Stance Classification.

Figure 1: GCC of follower network, colored by METIS score.

Following previous work on identifying controversial topics on social media
(Garimella et al. 2016, 2018b), we look for a bi-partitioning of the network
that would indicate polarization. We look for two sides of the debate because,
ultimately, the decision whether to wear a mask is binary. We start our
analysis by considering the follower network induced by the users captured in
our data, shown in Figure 1. In total, we are able to retrieve $509\,755\,293$
follower-followee relationships, such that followers are all users whose
tweets relate to masks. When we constrain the followees to the set of users in
our tweet dataset, the network contains $|V|=$598\,792$$ users and
$|E|=$35\,763\,336$$ edges. We use the graph partitioning algorithm METIS
(Karypis and Kumar 1998) to partition the network into two groups, repeatedly
$N=100$ times with different random seeds, so to get an ensemble of partition
assignments for each node, and use the average partition assignment for each
node across the $N$ repetitions as a polarity score $p\in[0,1]$ (Mejova et al.
2022).777Out of the two possible relabeling of each partition assignment, we
choose the one that minimizes the distance from the current node labeling to
maintain the partition identity fixed. We tune the relative size of the
partitions by maximizing the number of users within $95$% confidence interval
of either extreme, and find the optimal proportion to be $1.1$:$1$ of pro-mask
to anti-mask users. As the algorithm does not indicate the actual stances of
the users (in fact, it uses no content information whatsoever), each partition
is assigned its stance manually by examining 10 sampled users on each side.
All users in the same partition expressed the same stance, which suggests a
good separation. The sampled users’ stance is then propagated to all users
within their partition, $0$ for pro-mask and $1$ for anti-mask.

We also verify the robustness of our result by looking at the community
structure of the network. The top-4 communities found by the Louvain algorithm
(Blondel et al. 2008), which include more than $90\%$ of the nodes of the
network, have median polarity scores of $0.2,1.0,0.0,1.0$, which indicate
clear separation and polarization in their composition. The largest community,
which includes $18\,708$ users and is mainly pro-mask, has a slightly larger
spread of polarity scores, which may indicate a larger divergence of opinions
(and possibly doubts) in the population. This result can also be seen in
Figure 4a, the histogram of polarity scores of the network, which shows a
smoother decline on the left side than on the right side. Finally, we assign a
discrete label to each node according to its polarity score. We heuristically
choose the threshold $0.05$, which allows us to assign a label to $56.4\%$ of
users: $28.8\%$ with 0 and $27.6\%$ with 1, thus leaving $43.5\%$ of users
with an unknown label.888The 95% Agresti-Coull confidence interval upper bound
for binomial proportions of a polarity score of $0$ is $0.0444$, which we
round to $0.05$. The roughly equal shares of pro- and anti-mask users are in
line with recent other manual annotation work on this topic (Cotfas et al.
2021).

The two sides are colored as blue and red, and unknown as grey in Figure 1. To
assess the accuracy of the approach described so far, we sample 60 users from
those assigned a stance label by METIS and annotate them manually. To
determine the manual label, we use all captured tweets by each user, thus
considering in aggregate $1866$ tweets. The annotation was done by three
authors, all knowledgeable in the U.S. mask debate. Twelve percent of the data
was used for inter-annotator agreement, which shows perfect agreement. The
exercise revealed an overall precision of $86.4\%$ (95% confidence interval
[0.76, 0.94]), with perfect precision for pro-mask class, but only $72.4\%$
for anti-mask case, with several users incorrectly labeled as anti-mask by the
algorithm. The network structure suggests some connection of people who
express doubts but are not clearly anti-mask with more extreme positions.
Indeed, it is likely that the anti-mask stance is more diverse than the pro-
mask one, and cannot be easily represented with a single stance, similarly to
the vaccine hesitancy case (Cossard et al. 2020).

Figure 2: Daily time series of number of tweets by users classified by their
mask stance. Vertical line on mandate date.

Figure 2 shows the daily volume of tweets by users classified as either pro-
or anti-mask. The two time series are highly correlated, with a Pearson
correlation of $r=0.938$. The engagement begins roughly at the time of the
first CDC recommendation to wear masks on 2020-04-03. The increase in volume
confirms a previous study which finds increased “appetite” to share opinions
after major mask-related news (Cotfas et al. 2021). The subsequent peaks often
revolve around major news stories involving masks, such as one when the U.S.
Vice-President Pence visited a hospital without wearing a mask towards the end
of April,999https://www.washingtonpost.com/politics/pence-meets-with-mayo-
clinic-patients-staff-while-not-wearing-face-
mask/2020/04/28/57c4200c-897e-11ea-9dfd-990f9dcc71fc_story.html continuous
comparisons of the masking behavior of the two contenders for the U.S.
Presidency in late
May,101010https://edition.cnn.com/2020/05/26/opinions/biden-mask-trump-
ghitis/index.html and subsequent adjustments to the guidelines by the public
health officials who were trying to “correct” their previous messaging in mid-
July.111111https://edition.cnn.com/2020/07/12/politics/jerome-adams-surgeon-
general-mask-mandate/index.html Given the high correlation of the two time
series, we ask whether this effect is endogenous, i.e., if there is a causal
feedback loop whereby one of the two stances answers the other. A Granger
causality test on these two time series for time lags from 1 to 14 days finds
no strong relationship in either direction. The likely implication of this
negative result is that the volume of both stances has a common cause that is
exogenous: external events that get discussed on Twitter. Although we cannot
exclude an effect with lag shorter than one day, the fact that the networks of
two stances are well separated makes this hypothesis less likely.

Figure 3: Fraction of anti-mask users over the total geolocated users in the
48 U.S. states.

Figure 3 shows the fraction of anti-mask users in each state. We find a
particularly high concentration in the south, as well as some western states.
These findings largely agree with contemporaneous studies of mask-wearing
around the U.S. (Katz, Sanger-Katz, and Quealy 2020).

(a) Polarity score distribution

(b) Pol. leaning per mask stance

Figure 4: Distributions of (a) polarity scores of users (computed via METIS),
and (b) political leaning of users (inferred via follower relationships on
Twitter) grouped by inferred stance on mask-wearing.

#### Political Leaning.

As outlined in the Introduction, masking regulations have been strongly
politicized. According to PEW, when surveyed in 2020, Republicans were more
likely to call masks unnecessary, ineffective, oppressive, or unfair and were
much less likely than Democrats to be concerned about unknowingly spreading
COVID-19 to others.121212https://www.pewresearch.org/fact-
tank/2020/10/29/both-republicans-and-democrats-cite-masks-as-a-negative-
effect-of-covid-19-but-for-very-different-reasons/

We compare the mask stance of the users we are able to classify to their
political affiliation, which can be glimpsed via their Twitter social network.
In particular, we consider the Twitter accounts with known political leaning
that the users follows. In accordance to previous work, we assume that users
mostly follow accounts that are in agreement with their political views
(Golbeck and Hansen 2011), and create a list of prominent political accounts
in order to propagate their leaning to their followers. The list, which we
make available to the research community,131313Available at
https://tinyurl.com/poliaccounts includes 501 accounts of members of the U.S.
Congress, 79 governors, 70 party entities, and 67 Attorney Generals, as well
as 157 media accounts from allsides.com,141414https://www.allsides.com/media-
bias/media-bias-ratings and 67 journalists from
politico.com.151515https://www.politico.com/blogs/media/2015/04/twitters-most-
influential-political-journalists-205510 In total, it has a rather balanced
set of 487 right-leaning and 454 left-leaning accounts. Note that we do not
use prominent politicians such as the U.S. President, as such accounts may be
followed purely out of their popularity or importance. For each user in our
mask-related dataset, we count the number of accounts they follow in our list:
right-leaning $N_{R}$ and left-leaning $N_{L}$. We consider only users who
follow at least 5 accounts in our list from either side, and calculate the
aggregated political leaning score as $S_{PL}=(N_{R}-N_{L})/(N_{R}+N_{L})$,
which results in $S_{PL}\in[-1,1]$ with $1$ the most right-leaning score.
Thus, we are able to identify the political leaning of $18\,422$ users.

Figure 4b presents the distribution of political leaning scores for the two
categories of users based on their stances on masks. Pro-mask users are more
likely to be following left-leaning accounts, and anti-mask ones the right-
leaning ones, with almost no users existing in the middle political ground. In
fact, we find a strong polarization at the extremes of the political spectrum,
especially for anti-mask users.

#### Moral Values.

Moral values are directly linked to decision making (Karandikar et al. 2019).
Since there is an altruistic component to mask wearing, we ask what are the
moral values expressed by the holders of the two stances. We assess the moral
narratives by employing the MoralStrength lexicon (Araque, Gatti, and Kalimeri
2020), which holds the state-of-the-art performance in moral text prediction.
MoralStrength lexicon provides, along with each lemma, the Moral Valence
score, a numeric assessment that indicates both the polarity and the intensity
of the lemma in each moral foundation. According to this lexicon, the Moral
Valence is expressed in a Likert-scale from 1 to 9, with 5 to be considered as
neutral. Scores lower than 5 reflect notions closer to Harm, Cheating,
Betrayal, Subversion, and Degradation, while values higher than 5 indicate
Care, Fairness, Loyalty, Authority, and Purity. For each lemma in a tweet and
for each foundation, we obtain a moral valence score which is then averaged
for each tweet. Negation correction was not applied, as foundation polarities
do not directly translate as opposites (e.g., “not care” is not the same as
“harm”). The MoralStrength lexicon has a limited linguistic coverage; as a
result only the 41.5% of the tweets were found to express a moral foundation.
For all the rest, we assigned the value 5, neutral point of the Likert scale.
This approach pushes the observed mean towards the center of the scale, but
captures the variability of the value across all documents (we discuss the
implications of this methodological step in Discussion). To assess statistical
significance, we use a Student’s t-test (with the Benjamini-Hochberg
correction for multiple hypothesis testing) on the scores obtained from tweets
written before and after the mandate date (2020-04-03) for each moral
dimension.

Figure 5 shows the mean moral value scores of each side in the periods before
and after the mandate. Before, the two sides display comparably similar
values, except for _care_ , which is by far higher for the pro-mask side
(significant at $p<0.001$). However, there is a clear shift in the moral
narratives expressed after the mandate by both sides of the debate. To
understand the context of these morally-charged expressions, we examine a
sample of tweets for each side and value.

First, we find an increase in the valence of _authority_ for the anti-mask
side ($p<0.001$), which is mostly accompanied by criticism and mistrust of the
decisions made by the authorities. For instance, the anti-mask side associates
wearing a mask with weakness and lack of leadership (“@JoeBiden Real
leadership? With that thing on you look feeble”). Conversely, the pro-mask
side sees a lack of leadership in former President Trump’s refusal to wear a
mask (“Real leaders won’t mind when the mask smears your absurd orange
makeup”). Most of the examples we find in this moral category are indeed
criticism of the authorities, which nevertheless signifies that authority is
held in high importance, especially for the anti-mask side (it is the value
with the highest valence). The fact that post-mandate the authority-related
keywords have higher valence on the anti-mask side suggests stronger criticism
of the authorities than the pro-mask side (for whom the increase is
significant only at $p=0.004$ before the correction).

Figure 5: Moral valence in narratives expressed by pro-mask and anti-mask
users in the periods before (lighter points) and after (darker points) the
mandate. Dot represents the median value while the whiskers represent 5-95%
quantiles.

In terms of _care_ , both sides have a downwards shift after the mandate. For
the pro-mask side, this shift is accompanied by an increase in _fairness_ and
_loyalty_ , which can be interpreted as a shift in focus from personal choice
based on caring for others to complying with the mandate. Also, in this case,
the spotlight is often on the opposite side (“Masks protect others, which
Trump doesn’t care about. He cares only about himself.”). Conversely, anti-
mask supporters express themselves by prioritizing much less the notion of
_care_ , explicitly showing disregard for the protection of others, or simply
stating that they do not care about being criticized for not wearing a mask
(“I’m tired of being called a murderer because I don’t wear a mask. Cannot
understand how people are such thoughtless idiots”).

In addition, pro-mask supporters express significantly more _loyalty_ in their
messaging after the mandate ($p<0.0001$). Upon examining a sample of posts, we
find the increase is primarily related to criticism of those not wearing masks
as loyalists to a political affiliation or to Trump personally (“People don’t
wear masks because of Trump. Dear leader doesn’t wear one, loyal followers go
along”).

After the mandate, the _fairness_ value increases for both sides (both at
$p<0.0001$). Narratives about the fair treatment of individuals appear to be
present equally on both sides, focusing on negotiation when wearing masks is
reasonable (pro-mask: “What if a person not wearing it understands the
risks?”), comparison with other rights violations (pro-mask: “The only
“rights” that are violated is just not wearing a mask? They don’t appreciate
the nice life they have.”), or whether the criticism is fair (anti-mask:
“Colorado Governor Says People Who Refuse to Wear Mask are “Selfish Bastards”
OK, and anybody who wants others to harm their immune system is a POS Marxist
propagandist”). Instead, the valence of _purity_ does not change
substantially. We find that the notion of purity is not tied to religious
views, but to what people consider ‘natural’ and ‘healthy’ (e.g. “My 8 y.o.
niece will be heading back to school but will have to wear a mask! How is it
physically or emotionally healthy?”).

We further examine the evolution of moral values expressed by the two sides in
time and model it via an Interrupted Time Series (ITS) linear model. Figure 6
depicts the evolution of the average moral foundation score per side, with a
vertical dashed black line indicating the date of the official mask mandate.
Complementing our previous analysis, the interrupted time series model shows
that for all the moral dimensions, after the mandate, there is an evident
change in behavior by both sides. Perhaps the most interesting moral dimension
is _loyalty_ , whose signal is evidently diverging for two sides exactly after
the mandate date and continues the same trend until the end of our data
collection. We also observe that not only does the value of _care_ decreases,
the trend is downward over time, signaling a progressive shift in the debate.
Similarly, the value of _purity_ has a progressively negative trend for pro-
mask side over time. Thus, we find that the temporal dimension of the data can
be instructive about the evolution of the rhetoric in terms of divergence
between the two sides of conversation and changes in emphasis.

Figure 6: Time series of moral value scores of pro-mask and anti-mask users,
along with an interrupted time series analysis model.

#### Collectivism vs Individualism.

Table 2: Use of singular and plural personal pronouns in a tweet by side, before and after the mask mandate.  Pronoun | Singular | Plural
---|---|---
Mandate | before | after | before | after
Pro-mask | 0.55 | 0.48 | 0.11 | 0.10
Anti-mask | 0.53 | 0.60 | 0.09 | 0.10

One of the main purposes of mask wearing is the protection of others, an
expression of solidarity within the in-group against an external threat. Thus,
we turn to the Individualism-Collectivism (IC) dimension (Hofstede 2001),
which captures the standing of individuals as interdependent members of a
collective. We operationalize it via the personal pronouns used in the tweets,
mainly first-person singular (“I”, “me”,“mine” etc.) and first-person plural
(“we”, “us”, “ours” etc), following existing literature (Twenge, Campbell, and
Gentile 2013).

Table 2 shows the average usage of the two sets of pronouns in the tweets
posted by the two sides of the debate, separately before and after the mask
mandate. In the period before mask mandate, anti-mask supporters use _I_ and
other singular pronouns at 0.53, and after the prevalence increases to 0.60
($+0.07$, $p<0.0001$), which points to an increased focus on the individual.
Instead, pro-mask supporters decrease their usage of singular pronouns
($-0.07$, $p<0.0001$). The mention of _We_ and other plural pronouns by both
pro- and anti-mask supporters remain at the same levels ($\pm 0.01$), thus
indicating no significant shifts in the focus on the self-identified group.
Thus, although having comparatively similar usages of singular pronouns before
the mandate, the debate after the government’s messaging becomes more
individualistic for anti-mask side and less so for advocates of masking.

#### Information Environment.

Finally, we turn to the information context where the debate takes place, in
form of links to external sources and information from peers. We begin by
performing basic normalization steps on the original tweet text, including
removing punctuation, accents, contractions, and stopwords, substituting
emojis with a text description,161616Using emoji library
https://pypi.org/project/emoji/. and finally by performing lemmatization. We
then identify the most distinguishing words used by each side by calculating
the odds ratio of using particular words (lemmas) by one side compared to the
other before and after the mask mandate. We filter these words by their total
frequency since otherwise rare terms would emerge as most distinguishing.
Because the periods before and after the mandate have significant differences
in volumes, we set the word frequency threshold to $100$ and $800$ for the two
periods, respectively. Politician names appeared in the top terms and were
grouped together, hence resulting in two-word terms. The resulting top terms
are:

  * •

Pro-mask: _trumpvirus, Louie Gohmert, Putin, Herman Cain, wearadamnmask,
penny, Mayo, GOP, DeSantis, covidiots_

  * •

Anti-mask: _riot, micron, virtue, leftish, loot, sheeple, unhealthy,
bacterium, MSM, antifa_

The most distinctive words by each side are highly politicized: aside from
references to politicians (similar to other recent findings around masking
(Sanders et al. 2021)), we find several words used to attack the other side.
For instance, pro-maskers often use the term ‘trumpvirus’ to refer to
COVID-19, as a political response against the term ‘chinavirus’ used by
President Trump: “$@$realDonaldTrump #HermanCainRIP one more death due to
#TrumpVirus . Just think. If only he wore a mask and NOT attended the Tulsa
#CoronavirusRally”. The focus on individuals is worth of notice, as in the
case of Luie Gohmert, who strongly supported the use of hydroxychloroquine and
attended a House Judiciary Committee hearing without wearing a mask, or in the
case of Herman Cain, a Republican politician who opposed the mask mandate and
later died of
COVID-19.171717https://en.wikipedia.org/wiki/Herman˙Cain#Health˙and˙death
Similarly, the derogative term ‘covidiots’ is used to describe anti-mask
supporters.

The opposite side instead focuses on the ‘riots’ that would happen if lockdown
and mask mandates are enforced, and on the claim that masks can stop a
‘bacterium’ but not a ‘micron’-size virus. There are also claims that masks
are ‘unhealthy’ as they impede breathing, and are instead just ‘virtue
signaling’ devices: “Virtue signaling: mask and gloves. People lived with
coronaviruses for 100+ years. Turn off the TV.” A right-wing, anti-
establishment sentiment can be inferred from the reference to ‘sheeple’: “And
the CDC telling everyone to mask up is just another test to see how long the
sheeple will obey. #Globalists”, and from references to supposedly derogative
terms such as ‘leftish’ and ‘antifa’. Nonetheless, what is labeled as the
anti-mask side is also more varied in its opinions, ranging from just hesitant
to fully conspiratorial, somewhat similar to the spread of opinions around
vaccines (Cossard et al. 2020).

Table 3: LDA topics with the most representative words, extracted separately from tweets for each mask debate side. The topic prevalence is reported in parenthesis. |  Topic | Words
---|---|---
pro |  T1 (.35) |  distance, wear, social, day, mandate, today, friend, hope, state, stay
T2 (.33) |  wear, people, virus, covid, bad, protect, medical, infect, spread, science
T3 (.31) |  wear, Trump, Cain, rally, covid, Herman, die, Tulsa, death, kill
anti |  T1 (.46) |  wear, people, covid, virus, work, distance, die, thing, protect, spread
T2 (.31) |  wear, mandate, people, state, store, vote, business, leave, today, order
T3 (.22) |  man, Trump, rally, school, eye, fuck, kid, hope, stupid, big

Moving to higher-level constructs, we aim to uncover common patterns in the
argumentation proposed by both sides by applying a topic modeling approach
base on latent Dirichlet allocation (LDA) (Blei, Ng, and Jordan 2003).
Limitations of LDA clustering of short text are known, still, it offers a good
compromise between clustering performance and computational cost (Qiang et al.
2020).181818Using the Gensim library https://radimrehurek.com/gensim To derive
the optimum number of topics $k$, we optimize the topic coherency ($C_{v}$
metric (Röder, Both, and Hinneburg 2015)) models with $k\in[2,10]$. For both
the pro-mask and anti-mask sides, the model with $k=3$ is the best fit. Table
3 presents the salient keywords that form the corresponding topics. The topics
are ranked according to their prevalence, with T1 the most prevalent one, and
similarly, the terms are ranked by descending importance for the specific
topic.

From the emergent topics, the most prominent one on the pro-mask side concerns
the various interventions, including _social distancing_ and _wearing_ a mask.
(“COVID is not a flu! Everyone needs to wear a mask to protect others from
these germs.”), echoing our earlier finding of higher care value of this side.
The second topic includes references to _medicine_ and _science_ , while the
third centers around (Republican) political figures. On the anti-mask side,
the most prominent topic also concerns the interventions, but instead focuses
on whether interventions _work_ against the _spread_. The second one puts the
mandate in the context of the _businesses_ and _stores_ , in contrast with T1
from the pro-mask side which speaks about other interventions such as social
_distancing_ and _staying_ at home. The third topic is about political
_rallies_ , and argues that masks are not useful (since the _eyes_ are exposed
to the virus: “Still, even with a mask, you are uncovering the mucous
membranes in your eyes”).

Table 4: Counts of the top 30 URL domains posted by pro- and anti-mask users. Domains colored by class: news and news aggregators (black), social media and social media automator/aggregators (red), business platforms (blue), medical organization (green). Pro-mask | Anti-mask
---|---
rawstory.com | 2317 | youtube.com | 3485
cnn.com | 2000 | thegatewaypundit.com | 1341
youtube.com | 1751 | etsy.me | 1210
washingtonpost.com | 1393 | instagram.com | 912
a.msn.com | 935 | foxnews.com | 903
apple.news | 872 | zazzle.com | 796
huffpost.com | 758 | breitbart.com | 781
news.yahoo.com | 686 | nypost.com | 472
flip.it | 630 | fineartamerica.com | 453
nytimes.com | 587 | fxn.ws | 393
nbcnews.com | 573 | westernjournal.com | 362
thehill.com | 527 | dlvr.it | 344
dailykos.com | 486 | pixels.com | 317
instagram.com | 458 | buff.ly | 288
businessinsider.com | 449 | theblaze.com | 282
thedailybeast.com | 402 | bizpacreview.com | 268
newsweek.com | 371 | infowars.com | 250
theguardian.com | 343 | etsy.com | 246
usatoday.com | 337 | ift.tt | 236
yahoo.com | 333 | cnn.com | 231
cnbc.com | 307 | twitchy.com | 217
politico.com | 301 | ebay.us | 202
newsbreakapp.com | 295 | ncbi.nlm.nih.gov | 196
buff.ly | 260 | facebook.com | 192
npr.org | 252 | nejm.org | 191
politicususa.com | 236 | newsbreakapp.com | 175
apnews.com | 231 | a.msn.com | 173
nypost.com | 223 | google.com | 162
latimes.com | 209 | dennismichaellynch.com | 152
mol.im | 208 | aapsonline.org | 142

Finally, the two sides of the debate have about the same proportion of tweets
with URLs (around 23%). Earlier works have shown a polarization in terms of
news sources accompanying political polarization (Garimella et al. 2021),
however here we find differences in behavior beyond sharing news media. Table
4 shows the top domains of the URLs posted by pro- and anti-mask users, along
with the counts. Pro-mask users overwhelmingly post URLs pointing to news
websites or aggregators. YouTube and Instagram feature prominently in both
lists, though anti-mask users favor YouTube more than twice the second most
popular domain. Anti-mask tweets also link to a variety of business platforms,
including Etsy and Ebay, and lesser-known ones such as Zazzle, a platform for
custom-designed products. In addition, anti-mask users link to the
governmental agency National Center for Biotechnology Information (NCBI), the
New England Journal of Medicine (NEJM), and the Association of American
Physicians and Surgeons (AAPS). These findings stand in contrast to a smaller
recent study of geolocated-only tweets (He et al. 2021) that finds that anti-
mask tweets were less likely to share external information from public health
authorities. An explicit comparison of the captured tweet sets would be
necessary to resolve these findings.

## Discussion & Conclusions

Our analysis reveals that the government messaging about mask wearing
provoked—instead of the intended focus on the benefits to the communities and
society at large—a marked shift in the moral values towards higher
politicization of the issue, with an increased focus on authority and
fairness. We argue that interventions targeted to those resistant to mask-
wearing should center around these values, instead of appealing to those
valued less as the debate goes on.

It is no surprise that we find the mask debate captured on Twitter to be
highly politically polarized. It is well-known that polarization in the U.S.
political scene has been growing in the last
decades,191919https://www.pewresearch.org/politics/2021/11/09/beyond-red-vs-
blue-the-political-typology-2/ and this growing polarization has important
effects on several areas, including public health. As polarization and health
behaviors intersect, it is crucial to understand their interaction to design
effective health policies. For instance, the ideological and moral facets of
health intervention perceptions are closely related to the compliance in the
population, and the resulting effectiveness, as shown for instance in recent
statistics relating vaccination uptake to political
leaning.202020https://acasignups.net/21/07/20/update-us-covid19-vaccination-
levels-county However, shifting established associations between health-
related beliefs and political leaning may be challenging. For instance, the
attendees of Donald Trump’s rally in August 2021 booed him after he voiced
support for the COVID-19 vaccination
drive,212121https://www.theguardian.com/us-news/2021/aug/22/donald-trump-
rally-alabama-covid-vaccine thus illustrating that even the forerunners of the
Republican party may encounter challenges in connecting with their
constituents. As the pandemic develops, and more mandates are issued by the
governments, constant polling and monitoring is essential in establishing the
public response to these measures (as of mid-2021, the attitudes toward
masking are still highly
polarized).222222https://www.politico.com/news/2021/08/02/poll-americans-back-
return-of-masking-502144

We find that different moral values underpin the reasoning emphasized by the
two camps. Pro-mask arguments highlight loyalty and fairness while criticizing
the opposing leadership. The anti-mask ones, on the other hand, focus on the
authoritarian and oppressive aspects of the mandate and show a lack of concern
for the effects of their actions on others. In accordance with these results,
when examining this phenomenon through the lenses of the Collectivism-
Individualism theory, we notice a decisive shift of the anti-mask community
towards individualism, with more intense use of first-person personal
pronouns. We note the lack of loyalty among the values emphasized by the anti-
mask side, which tends to hold a conservative political view, and differs from
the commonly observed ones associated with conservatism: authority, loyalty,
and purity (Kivikangas et al. 2021; Fulgoni et al. 2016). This may be a
response to the pro-social framing of the mask intervention, thus leading its
opponents to de-emphasize the in-group narrative usually common to their side.
This interpretation may point to motivated reasoning, wherein the desired
conclusion modifies the worldview usually taken. Interestingly, the ITS
analysis shows divergence over time between the two groups on the value of
loyalty, which is alarming since such disagreement, if not adequately
addressed, can lead to severe societal polarization.

This polarization seems to have been accompanied by a lively commercial
activity. When examining the links posted by the two sides of the issue, we
find a prominent existence of commerce-related platforms including _etsy.com_
, _ebay.us_ , as well as platforms for custom creation of merchandise such as
_zazzle.com_. Indeed, a brief search on these websites reveals an assortment
of t-shirts, coffee mugs, face masks, and baby bibs with political messages
from both sides of the debate. Sometimes historical symbols were used to make
a stance, such as the use of the yellow star—like those forced on Jews by Nazi
Germany—which was sold by a Nashville store protesting against the vaccination
campaign. After community criticism, the item was
removed232323https://www.bbc.com/news/world-us-canada-57297902. Our findings
suggest that there is an active development of symbolism and aesthetics of the
resistance movement, and it would be a fascinating subject of research to
uncover the non-verbal representations of the self and the group, expressions
of values, and calls to action (McGarry et al. 2019; Awad and Wagoner 2020).
Awareness of such symbolism and self-conceptualization is vital for crafting
appropriate messages and fostering communication between the two sides.

Despite the volume and span of the data analyzed here, this study has some
limitations as most social media studies. Especially when concerning
politically charged topics, often a minority of vocal users dominates the
conversation (Mustafaraj et al. 2011), thus making the opinions of the “silent
majority” challenging to discern. The observed relationships between the
stances on masking, moral values, and political stance are limited to those
who choose to express themselves vocally. In addition, we use a high-precision
vocabulary to measure moral values, which has the side effect of pushing the
average valence in our results close to the neutral point, due to a large
number of tweets for which we cannot extract moral valence. Traditional
surveys are necessary to reach those not as comfortable expressing their
opinions online; however, even those have selection biases. The automated
tools utilized in this study are not perfect: after manual examination, we
find that the network-based classifier achieved an accuracy of 72.4% for the
anti-mask class, thus introducing noise in the subsequent analysis. However,
from the experience of manually labeling the users, we postulate that the
complexities of human expression, including humor and sarcasm, may limit the
best possible performance of such a classifier. Similarly, the inference of
moral values from the text may struggle with vocabulary mismatch, sarcasm, and
self-censorship. Finally, the generalizability of this study is limited by the
unique circumstances of an unprecedented global pandemic happening in a hyper-
connected world during a politically polarized social environment. These
considerations must be taken into account when comparing our findings to new
scenarios.

### Ethical Considerations

The dataset presented here contains only tweets which were publicly available
at the time of the collection. We make the dataset publicly available to the
research community in compliance with the Twitter Terms of
Service,242424https://developer.twitter.com/en/developer-terms/ that is,
sharing only the tweet IDs of the collected posts, which will have to be re-
collected. In this paper, we have rephrased all quoted tweets to prevent re-
identification of their authors. This practice ensures that the tweets which
have been removed (either by the user or the platform) will not be available.
Although large, this dataset does not include users with particular
disabilities which may disallow them to interact with the platform, as well as
minors and those blocked by Twitter. On the other hand, the content collected
here affects not only those who have posted it, but also those who viewed or
interacted with it, which may be orders of magnitude more users, since most
users are “lurkers” who consume social media content without posting (Van
Mierlo 2014). Ultimately, the masking decisions made by people engaging in
this deliberation may directly affect the health and life of vulnerable
people, such as those with autoimmune disorders or other conditions making
them especially vulnerable to COVID-19. Additionally, the sometimes aggressive
rhetoric in this material may not be suitable for young Twitter users, or
those dealing with mental health issues. Also, although the focus of this
study is the moral dimension of the debate, we caution public health
communicators not to overemphasize moral or emotional dimensions of their
message (or attempt to emotionally manipulate their audience), but rather
provide the clearest and most informative messaging possible. Further, we
would like to discourage the tools used in this study to be used for targeting
individuals espousing particular opinions for harassment or undue
surveillance, and to follow the AAAI ethical
guidelines252525https://www.aaai.org/Conferences/code-of-ethics-and-
conduct.php in the application of these findings.

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