# Parlermonium: A Data-Driven UX Design Evaluation of the Parler Platform

Emma Pieroni  DePaul University243 S Wabash AveChicagoIL60604USA
<EMAIL_ADDRESS>,  Peter Jachim  DePaul University243 S Wabash
AveChicagoIL60604USA<EMAIL_ADDRESS>,  Nathaniel Jachim  University of
Michigan243 S Wabash AveDearbornMI60604USA<EMAIL_ADDRESS>and  Filipo
Sharevski  DePaul University243 S Wabash AveChicagoIL60604USA
<EMAIL_ADDRESS>
(2021)

###### Abstract.

This paper evaluates Parler, the controversial social media platform, from two
seemingly orthogonal perspectives: user design perspective and data science.
UX design researchers explore how users react to the interface/content of
their social media feeds; Data science researchers analyze the misinformation
flow in these feeds to detect alternative narratives and state-sponsored
disinformation campaigns. We took a critical look into the intersection of
these approaches to understand how Parler’s interface itself is conductive to
the flow of misinformation and the perception of “free speech” among its
audience. Parler drew widespread attention leading up to and after the 2020
U.S. elections as the “alternative” place for free speech, as a reaction to
other mainstream social media platform which actively engaged in labeling
misinformation with content warnings. Because platforms like Parler are
disruptive to the social media landscape, we believe the evaluation uniquely
uncovers the platform’s conductivity to the spread of misinformation.

††copyright: acmcopyright††journalyear: 2021††doi: doi

##  1\. Introduction

Since the rise of social media in the United States, there has been a fierce
debate over the role, legal and societal, of media companies in regulating and
moderating speech on their platforms (Balkin, 2004). With the First Amendment
of the U.S. Constitution protecting freedom of speech, some view means of
platform moderation as abridging that right (Hooker, 2019). This debate came
into acute focus during the 2020 U.S. Election cycle, when Twitter and
Facebook escalated use of warnings and deleted content in which it deemed
misleading or harmful to public discourse (Roth and Pickles, 2020). Seeking to
escape the “policing” of these platforms, many fled to a relatively low-
profile platform named Parler, which dedicates itself to “free speech-driven”
policies and prides itself as a “public square” of opinions (Jekielek, 2020).
An early hint to such a “platform migration,” although in the opposite
direction, was noticed from alt-right communities within 4chan and Reddit
toward Twitter (Zannettou et al., 2017). In this paper, our objective is not
to analyze the debate over social media moderation nor the flow of alternative
narratives between platforms, but rather, to understand how a platform’s
interaction with “free speech” can manifest itself in the user design and
reflect on its audience.

What happened inconsistently and irregularly before the 2020 U.S. election,
culminated in the aftermath, when misinformation about the legitimacy of the
election spread rapidly across many social media platforms (U.S. Cybersecurity
& Infrastructure Security Agency, 2020). Mainstream platforms like Facebook
and Twitter began adding labels to President Trump’s account content
regularly. The intentions and incentives of social media companies in these
instances are outside the purview of this paper, but moderation like this has
contributed to a majority of Americans –and in particular, Republican-leaning
Americans– who believe that social media companies censor certain political
perspectives (Vogels et al., 2020). Unlike other social media platforms,
Parler stood out, as it elected to keep content on its platform that had been
otherwise removed by large Silicon Valley companies. Those wary of excessive
censorship began to flock to Parler, boasting “banned by the mainstream media”
as a badge of honor, and as more people migrated off mainstream platforms,
Parler became a safe haven for all opinions, as it became host to a wide range
of conspiracy theories and fringe alternative groups. Naturally, in the quest
to understand the intricacy of how trolling narratives develop on social
media, it became clear we needed to investigate this previously-obscure
platform further, which brings us to the topic of this paper: A data-driven UX
design analysis of the Parler platform to uncover the attraction of the site
as well as any intentions to target that appeal at specific groups of users.

##  2\. Parler: Aesthetics

###  2.1. Rustic Appearance

Parler was founded in 2018, although it didn’t gain large popularity until
2020. Since the platform has been live for multiple years, to some extent,
Parler’s design and development teams have had ample opportunity to fix issues
that are deemed problematic, and have had time to implement ambitious design
features as they wish, as shown in Figure 1. This means that, to some extent,
we can assume that obvious glitches have been ignored for a reason or are
intentionally left alone. For example, the rounded corners on many pages
appear slightly jagged. Additionally, while a dark theme is offered, many of
the buttons in the dark theme do not appear to change color, forcing the
users’ eyes to re-adjust. The lack of polish on the site demonstrates the
clear disparity between Parler, and the conventional Silicon Valley social
media companies like Twitter and Facebook. Implicitly, this contrast
emphasizes Parler’s “outsider” status, appealing to users who deem themselves
“other” as well. A similar effort to stay distinctly outsider with even more
crude appearance of contrasting red/orange background and ruby font (the
“Yotsuba” style) or a similar dark theme (the “Tomorrow” style) could also be
observed on the infamous discussion board 4chan (Tuters and Hagen, 2020).

Figure 1. Parler: Home Page

###  2.2. Relative Anonymity

While viewing a parley, the small font size, profile picture, and lack of
color contrast pulls attention away from the source, allowing the user to
focus more intently on the content of the parley, regardless of source
reliability. Broadly, the potential for anonymity online allows users to open
up about their true feelings, without having to take responsibility, by
separating their actions from the real world and identity (Nissenbaum and
Shifman, 2017). By obscuring the identity of the poster of a parley, this
incentives users to post with more genuine intention, without the fear of
repercussions or criticism outside of Parler, while also encouraging users to
avoid source-checking the information they are absorbing while on the
platform. Relative anonymity is a desired feature for platforms supporting
progressive dissent, for example 4chan, which enables “ephemeral design” where
posts are deleted after a certain amount of user engagement (Bernstein et al.,
2011). The absence of persistent markers of identity and reputation, common to
social media platforms, enables Parler users, like users on 4chan, to
continually demonstrate their subcultural status of “others” and reformulate
the boundaries of their community (Nissenbaum and Shifman, 2017).

###  2.3. Importance of Images

When a user clicks on a profile page to learn more or investigate further, the
use of a monochromatic grey-scale between text and background provides little
contrast. So instead of drawing a user’s eye to text, a user is prone to focus
on the images. Visual symbols play a significant role in constructing
political narratives and can be used to evoke strong emotions surrounding a
political event that words cannot (Lilleker et al., 2019). Images are often
used to amplify an event, by adding drama, but can also serve as means to
identify an event or cause efficiently, through communicating specific
arguments (Schill, 2012). In the context of Parler, the focus on the large
profile banner gives a user a unique opportunity to present a compelling
argument –in a fraction of the time– through an image, rather than relying on
text to convey that narrative. Using the profile page for user QStorm1111,
shown in Figure 2, whose background image utilizes a combination of blue
lightning and large text, as well as a dark color scheme; this picture is
quite striking, and to a viewer, might elicit a narrative that there is some
dramatic or “biblical” change coming to Washington. A similar utilization of
images also can be observed on 4chan with the anonymous sharing of memes,
which allow otherwise complete strangers to demonstrate and negotiate in-group
belonging through their vernacular fluency (Nissenbaum and Shifman, 2017).

Figure 2. Parler: A Cover Photo Example

###  2.4. Color

The Parler platform is composed of a dominantly red and grey color scheme, in
which red is prone to elicit more powerful emotions than many other colors
(Elliot and Maier, 2014). But the specific emotions associated with a color
are dependent on the environment or context that the color is presented in; in
achievement or intense situations, red is used to evoke strong emotions, as it
is associated with negative connotations such as threat, danger, and
aggression – the competitive nature of social media may fit this context
(Elliot and Maier, 2014). Alternatively, as in the case of 4chan (the Yotsuba
style), the red color can signal the subculture status of antagonism. In both
cases, one could argue that the red is a deliberate choice of the dark pattern
design approach, geared toward (Gray et al., 2018).

Rather than moving a user’s eye across the page by using a tertiary color to
offset the red/grey color scheme, instead, users are drawn to the specific
elements which are highlighted in red or any additional features that take up
space on the page. Grey-on-grey text provides little contrast or visual
interest when reading, and even the deep red tone appears muted on its grey
background (also, red on grey is not color-blindness friendly; a condition of
color blindness as protanopia makes the red color appear black resulting in a
confusing look). Instead of focusing on the procedural information or dulled
text, attention is drawn to the images throughout the page, further
emphasizing the importance of visuals on the site.

##  3\. Parler: Functionality

###  3.1. Search Function

Unlike Twitter and even 4chan, where a user can search for a term or a word
and view tweets/posts that have used that term or word, in Parler, a user can
only search for usernames, or for specific hashtags. This has a couple of
different effects on the platform. The obfuscation of search terms helps
protect users from being searched for online, protecting unprotected speech.
While use of certain words can make a person unemployable, this means that
someone doing research cannot search to see occurrences of slurs or other
objectionable parlance that could be brought to, for example, an individual’s
employer. This provides a slim layer of protection for individual users,
allowing them to say what they like and only to make specific words
“searchable”. Additionally, this layer of protection can help encourage people
to make slanderous claims. For example, if Pfizer wants to see any
misinformation about their vaccine, they cannot simply search vaccine, but
have to follow specific individuals to see what they say about the vaccine.
This gives individuals like Alex Jones freedom to make claims that the
COVID-19 vaccines contain HIV, as shown in Figure 3, without risk of
litigation, when Pfizer was the only vaccine with FDA emergency use approval.

Figure 3. A Parler Post Claiming the COVID-19 Vaccine Contains HIV

The limited search functionality also affects the user’s freedom to
investigate alternate perspectives regarding specific events. Unlike Twitter,
where a user can search all tweets in the aftermath of an event to see a
variety of different perspectives, on Parler, users can only search for
parleys that have specific hashtags. The end result, is that users have much
less freedom to see a variety of viewpoints on events, but are rather confined
to comfortable and familiar hashtags. The ineffectiveness is psychologically
convenient, as instinctively, people tend toward homophily, or the tendency
for similar individuals to associate with each other more often than with
individuals viewed as “different” (Grevet, 2013). Somewhat contradictory to
Parler’s emphasis on “free speech”, the inability to find additional or
alternative perspectives, leaves users predestined to focus on one comfortable
narrative, cognitively segregated from other perspectives, as well as
inevitably leaving Parler to fail its own expectations of creating a “public
square”-style exchange of ideas.

###  3.2. People to Follow

There is a box on the right side that tracks a long list of accounts that a
user can follow as shown in Figure 4. A close inspection of these accounts
reveals that they belong to “others” or the alternative, self-proclaimed
campus of free speech defenders (Vogels et al., 2020). On the surface, this
resembles a common functionality across other social media sites. LinkedIn,
for example, suggests “People you may know,” and Twitter offers a list of “Who
to Follow.” The stark difference between these features is that once a user is
added from the “People you may know” or “Who to Follow” lists, that user is
removed from the list, and new recommended users are added. In contrast to
Twitter or LinkedIn, Parler’s functionality serves as a checklist, where the
checklist shows people who a user already follows, indicated with a red check-
mark, and continues to re-recommend the users who are not followed yet. This
can be interpreted as a progress-indicator, implicitly suggesting these are
the people a user should follow, in order to have a complete feed. Illustrated
by the popular “gotta catch ’em all” phrase from Pokemon, the Zeigarnik Effect
explains that incomplete tasks are more potent and memorable than completed
ones (Denmark, 2010), leaving a user more prone to continue playing Pokemon
or, translated in the context of Parler, continue following profiles until the
given task is completed.

Figure 4. Parler: People to Follow Frame

As shown in Figure 4, Parler’s “People to Follow” window consists of almost
entirely of users flaunting a yellow Parler badge, identifying them as
“verified influencers” or members of the Parler community with a large
following. By recommending these large influencers rather than regular
accounts, Parler amplifies the voices of its pundits rather than its users,
contradicting its CEO’s, John Matze, perspective that the objective of Parler
is to be a “public square for promoting discourse” (Jekielek, 2020). Rather
than other social media platforms that opt for the terms “likes”, “shares”, or
“retweets”, Parler has coined “echoes” to quantify interaction on a post, but
the interface’s focus on influencers takes the term to a new level, as it
turns the platform into an echo-chamber for its largest users (Grevet, 2013).
For example, an early analysis of the Parler structure found that about 40% of
the typical users have more than a single follower, whereas about 40% of gold
badge users have more than 10,000 followers (the verified influences fall
somewhere in the middle) (Aliapoulios et al., 2021).

##  4\. Data Analysis of Parler’s Content Flows

Given the isolated nature of the Parler platform, to build a feed to collect
data from, we elected to follow a variety of different accounts (including
@TheProudBoys, @InfoWars, @WarRoomPandemic, @KAGDonaldTrump, @qanonymous,
amongst others). Some of these accounts stand out as conservative thought-
leaders and were selected via Parler’s “People to Follow” feature, and another
portion were found via searching selected terms and hashtags including
#StopTheSteal, #AmericaFirst, #MAGA, and “qanon.” After expanding our network
on Parler, we compiled a dataset between September 2020 and January 21 2021
(before Parler was taken offline). In total, the dataset contains more than
200,000 words, representing opinions presented by 67 Parler users, and
approximately 10,000 parleys. To comply with Parler’s terms and conditions, we
manually copied and pasted data from accounts we followed, and pasted them
into an Excel file.

###  4.1. Correspondence Analysis

Our first approach to analyzing trending discussions on Parler was through the
correspondence analysis shown in Figure 5. A correspondence analysis takes the
eigenvectors of a contingency table based on the $\chi^{2}$ statistic. To
create the contingency table, we compared the top nouns and verbs used in
parleys. We parsed parleys into their parts of speech, and cleaned to just
reference their lemma. The first dimension, which accounts for 46.78% of the
variability in the data, indicates a forceful narrative around votes and the
Capitol, and a more hopeful and reflective narrative around Twitter and the
President. Higher values in the first dimension include scores for the nouns
“Capitol,” “America,” “People,” and “Vote,” while the verbs used are mostly
forceful verbs, like “make,” “take,” and “say.” Lower values include hopeful
and reflective verbs, like “will,” “know,” “want,” and “come,” as well as the
nouns “Twitter,” “President,” and “election.”

Figure 5. Parler Content Flow Correspondence Analysis

The second dimension, which accounts for 29.96% of the variability in the
data, suggesting that “Twitter,” “Parler,” and the “Capitol” are all part of a
movement with stronger, forceful verbs, while verbs used to describe votes,
people, America, and Biden are more optimistic (hopeful that Biden will be
somehow ousted). High values for the second dimension start with the noun
“Capitol” and continues to the noun “vote”, with higher values in the second
dimension describing “Twitter” and “Parler.” The verbs associated with those
nouns are forceful/challenging verbs, like “make,” “take,” ‘will,” and “come.”
Lower values in the second dimension seem to indicate a more positive,
aspirational interpretation of these nouns (it is, however, worth noting that
the positive association with Biden may, for example, have to do with how
Parler users “want” Biden to lose, “go” away, “say” Trump won the election,
etc.).

###  4.2. Regression Analysis

Our second analysis was performed using a regression analysis to see the
extent we could model the interactions between different words. This gives
some idea of the extent that all of the topics and verbs discussed were
related to one another. To model this information, we built a second-order
linear regression model that estimated the log of the total number of times
each noun-verb pair was used based just on the number of nouns and the number
of verbs in the contingency table created above. Here is the model used:

$ln(tpc)=-3.723+1.063e^{-3}*vc+1.873e^{-6}*nc-1.242e^{-7}*vc^{2}+9.991*log(nc)+\epsilon$

where $tpc$ is the total number of times that a noun/verb pair appeared, $vc$
is the number of times that verb was used, $nc$ is the number of times each
noun was used, and $\epsilon$ represents the residual errors that are not
accounted for by the model. The model accounted for 91% of the variability in
the total number of times each pair of words appeared, and had an adjusted
$R^{2}$ of 90.7%. In practical terms, this means, in addition to the specific
deviations discussed above, all verbs and nouns were used together quite
frequently as well. So, while “Twitter” and “Capitol” appeared separately in
the correspondence analysis above, they were discussed in similar contexts as
well. A close inspection of the content suggest the most dominant context may
be Twitter’s ban on Trump following the insurgencies on the Capitol building.

###  4.3. Linguistic Analysis

We performed a linguistic analysis of the collected dataset as well a subset
of the parleys containing the words “Twitter” and “Capitol”, shown in Table 1
(Tausczik and Pennebaker, 2010). The higher “analytical thinking” score
indicates that the overall parleys follow logical and considerate thinking
patterns. This is not surprising, given Parler’s somewhat “refined” reputation
compared to other sub-cultural platforms, e.g. 4chan. The Twitter/Capitol
parleys show an even higher score on the analytical thinking, indicating that
they are mostly direct “here-and-now” expressions. The high “clout” score
indicates the high level of confidence that Parler users display through their
writing. This high confidence stems from the beliefs and convictions to defend
“free speech” online. An even higher “clout” score of the Twitter/Capitol
parleys suggest even higher confidence, characteristic for the “verified
influencers” on the platform. The significantly low “authenticity” score
indicates that Parler users do not post authentic content, even in the case of
the Twitter/Capitol parleys. Rather, they repetitively regurgitate similar
narratives promulgated by the “verified influencers.” Finally, the “emotional
tone” score (below 50) indicates a negative and impolite style of writing
overall, characteristic for conspiracy theories, fake news, and
misinformation. The emotional tone of the Twitter/Capitol parleys, with the
highest negative difference in relative score, is considerably negative and
rude. This demonstrates the antagonistic sentiment about the Twitter
censorship decision.

Table 1. LIWC scores of the Parler Dataset. Dataset | Analytical Thinking | Clout | Authenticity | Emotional Tone
---|---|---|---|---
Parler Overall | 87.56 | 75.91 | 16.64 | 35.67
Twitter/Capitol | 91.72 | 83.83 | 16.17 | 20.96

##  5\. Conclusion

In this paper, we sought to investigate how UX design choices aid the
proliferation of alternative narratives on the Parler platform. First, we
analyzed the design and functionality features of the platform itself, and
then, collected and analyzed a dataset to understand how content flows on the
platform. Our findings suggest that Parler is especially conducive to
dissemination of misinformation through a variety of factors including
heightened user anonymity; the prioritization of images rather than parley
source/text content; emphasis on platform influencers; the absence of counter-
argumentation. The dominant red/grey color scheme of Parler’s user interface
elicits antagonistic connotations associated with the platform, a notion
supported by our correspondence and linguistic analyses, which suggested
overwhelming negative parley sentiment, particularly around the Twitter’s
decision for censorship following the failed coup on the Capitol. Designed for
a “public square” exchange of ideas, Parler fails in delivering that ideal,
and instead, it rewards and amplifies its largest users. Taking all these
things into account, Parler’s commitment to “free speech” seems largely one-
sided, encouraging the spread of unverified claims, rumors, and
misinformation.

## References

  * (1)
  * Aliapoulios et al. (2021) Max Aliapoulios, Emmi Bevensee, Jeremy Blackburn, Emiliano De Cristofaro, Gianluca Stringhini, and Savvas Zannettou. 2021.  An Early Look at the Parler Online Social Network.  arXiv:2101.03820v2 [cs.SI]
  * Balkin (2004) Jack M Balkin. 2004\.  Digital speech and democratic culture: A theory of freedom of expression for the information society.  _NyuL rev._ 79 (2004), 1.
  * Bernstein et al. (2011) Michael Bernstein, Andrés Monroy-Hernández, Drew Harry, Paul André, Katrina Panovich, and Greg Vargas. 2011.  4chan and/b: An Analysis of Anonymity and Ephemerality in a Large Online Community. In _Proceedings of the International AAAI Conference on Web and Social Media_ , Vol. 5.
  * Denmark (2010) Florence L. Denmark. 2010\.  Zeigarnik Effect.  (2010).  https://doi.org/10.1002/9780470479216
  * Elliot and Maier (2014) Andrew J. Elliot and Markus A. Maier. 2014.  Color Psychology: Effects of Perceiving Color on Psychological Functioning in Humans.  https://www-annualreviews-org.ezproxy.depaul.edu/doi/10.1146/annurev-psych-010213-115035
  * Gray et al. (2018) Colin M. Gray, Yubo Kou, Bryan Battles, Joseph Hoggatt, and Austin L. Toombs. 2018.  The Dark (Patterns) Side of UX Design _(CHI ’18)_. Association for Computing Machinery, New York, NY, USA, 1–14.  https://doi.org/10.1145/3173574.3174108
  * Grevet (2013) Catherine Grevet. 2013\.  Combating Homophily through Design. In _Proceedings of the 2013 Conference on Computer Supported Cooperative Work Companion_ (San Antonio, Texas, USA) _(CSCW ’13)_. Association for Computing Machinery, New York, NY, USA, 57–60.  https://doi.org/10.1145/2441955.2441971
  * Hooker (2019) Matthew P Hooker. 2019\.  Censorship, Free Speech & Facebook: Applying the First Amendment To Social Media Platforms Via the Public Function Exception.  _Wash. JL Tech. & Arts_ 15 (2019), 36.
  * Jekielek (2020) Jan Jekielek. 2020\.  Massive, Unexpected Growth on New Free Speech Platform, Bypassing Shadow Bans—Parler CEO John Matze.  https://www.theepochtimes.com/massive-unexpected-growth-on-new-free-speech-platform-bypassing-shadow-bans-parler-ceo-john-matze_2963526.html
  * Lilleker et al. (2019) D. Lilleker, A. Veneti, and D. Jackson. 2019.  Images matter: the power of the visual in political communication.  https://theconversation.com/images-matter-the-power-of-the-visual-in-political-communication-122281
  * Nissenbaum and Shifman (2017) Asaf Nissenbaum and Limor Shifman. 2017.  Internet memes as contested cultural capital: The case of 4chan’s /b/ board.  _New Media & Society_ 19, 4 (2017), 483–501.  https://doi.org/10.1177/1461444815609313
  * Roth and Pickles (2020) Yoel Roth and Nick Pickles. 2020.  Updating our approach to misleading information.  https://blog.twitter.com/en_us/topics/product/2020/updating-our-approach-to-misleading-information.html
  * Schill (2012) Dan Schill. 2012\.  The Visual Image and the Political Image: A Review of Visual Communication Research in the Field of Political Communication.  12, 2 (2012).  https://doi.org/10.1080/15358593.2011.653504
  * Tausczik and Pennebaker (2010) Y. R. Tausczik and J. W. Pennebaker. 2010.  The Psychological Meaning of Words: LIWC and Computerized Text Analysis Methods.  _Journal of Language and Social Psychology_ 29, 1 (2010), 24–54.
  * Tuters and Hagen (2020) Marc Tuters and Sal Hagen. 2020.  (((They))) rule: Memetic antagonism and nebulous othering on 4chan.  _New Media & Society_ 22, 12 (2020), 2218–2237.  https://doi.org/10.1177/1461444819888746
  * U.S. Cybersecurity & Infrastructure Security Agency (2020) U.S. Cybersecurity & Infrastructure Security Agency. 2020.  Roumor Control.  https://www.cisa.gov/rumorcontrol
  * Vogels et al. (2020) Emily A. Vogels, Andrew Perrin, and Monica Anderson. 2020\.  Most Americans Think Social Media Censor Political Viewpoints.  https://www.pewresearch.org/internet/2020/08/19/most-americans-think-social-media-sites-censor-political-viewpoints/
  * Zannettou et al. (2017) S. Zannettou, T. Caulfield, E. De Cristofaro, N. Kourtelris, I. Leontiadis, M. Sirivianos, G. Stringhini, and J. Blackburn. 2017.  The Web Centipede: Understanding How Web Communities Influence Each Other through the Lens of Mainstream and Alternative News Sources. In _Proceedings of the 2017 Internet Measurement Conference_ (London, UK) _(IMC ’17)_. Association for Computing Machinery, New York, NY, USA, 405–417.  https://doi.org/10.1145/3131365.3131390