11institutetext:  Hertie School of Governance, Berlin 22institutetext: Oxford
Internet Institute, University of Oxford, 33institutetext: Humboldt Institute
for Internet and Society, Berlin

# How the Far-Right Polarises Twitter:
’Highjacking’ Hashtags in Times of COVID-19

Philipp Darius 11 Fabian Stephany Corresponding Author: 2233
<EMAIL_ADDRESS>
###### Abstract

Twitter influences political debates. Phenomena like fake news and hate speech
show that political discourse on micro-blogging can become strongly polarised
by algorithmic enforcement of selective perception. Some political actors
actively employ strategies to facilitate polarisation on Twitter, as past
contributions show, via strategies of ’hashjacking’111The use of someone
else’s hashtag in order to promote one’s own social media agenda. For the
example of COVID-19 related hashtags and their retweet networks, we examine
the case of partisan accounts of the German far-right party Alternative für
Deutschland (AfD) and their potential use of ’hashjacking’ in May 2020. Our
findings indicate that polarisation of political party hashtags has not
changed significantly in the last two years. We see that right-wing partisans
are actively and effectively polarising the discourse by ’hashjacking’
COVID-19 related hashtags, like #CoronaVirusDE or #FlattenTheCurve. This
polarisation strategy is dominated by the activity of a limited set of heavy
users. The results underline the necessity to understand the dynamics of
discourse polarisation, as an active political communication strategy of the
far-right, by only a handful of very active accounts.

###### Keywords:

COVID-19 Hashtags Networks Political communication strategies #CoronaVirusDE
#FlattenTheCurve

##  1 Introduction

The Coronavirus pandemic was accompanied by a so-called ‘infodemic’. This term
refers to the acceleration of information and high uncertainty in the
information environment, as for instance, in science, the media or
policymaking. Moreover, there was a surge of dis- and misinformation and most
debated ‘conspiracy theories/myths’ during the pandemic that additionally
disrupted the media and information system. In this article, we show that far-
right actors used long established strategies such as ‘hashjacking’ using
political opponents hashtags and public hashtags to influence public opinion
formation and leverage their own content. In this article, we assess
polarisation strategies by partisans of the German right-wing populist party
“Alternative für Deutschland” during two observation periods in 2018 and 2020.
The analytical approach builds on community detection algorithms from social
network analysis and logistic regression models to determine the likelihood of
strategic hashtag hijacking or “hashjacking”.

The results indicate that far-right users and politicians have been successful
in establishing counterpublics on Twitter and were much more involved in
linking their messages to wider discourses than partisans of other parties. By
comparing the polarisation of party hashtags between 2018 and 2020, we
identify political partisan groups and show that polarisation of party
hashtags is relatively stable and highest for the far-right #AFD. Moreover,
our results indicate that Twitter debates on #CoronaVirusDE and
#FlattenTheCurve are strongly polarised, too. The group of accounts spreading
critical messages, questioning the existence of COVID-19 or the necessity of
measures taken against the virus, is populated by partisans of the far-right.
Within the far-right partisan movement around #AFD a handful of heavy users
are retweeting most of the content. It is important to know the dynamics of
social media discourse, when engaging in it: A small group of political
partisans, in our case of the far-right AfD, systematically polarises
discussions on public hashtags to influence public opinion formation and
leverage their own content.

##  2 Background

Social platforms have become central hubs and fora for political debates,
economic transactions, marketing and social communication. As a consequence,
social platforms take an increasingly important role in the formation of
public opinion, political campaigning and political news consumption. Yet, the
media environment can be described as hybrid [1], since television remains an
important source of information and media consumption for large parts of the
population [2]. Nevertheless, the share of citizens for which the Internet and
social platforms represent the most important channels for political news
consumption is increasing in many countries, e.g., Germany and the US.

For extreme parties and political opinions, social media offer additional
channels for political communication in which extreme political actors do not
need to follow the values and norms of traditional mass media and are thus
able to spread their respective ideology [3]. These alternative communication
channels (social platforms and messengers) are particularly attractive for
right-wing populist and radical-right politicians who often have a hostile
attitude towards established media and less access to traditional media
channels [3]. Thus, right-wing populist actors and movements benefited
disproportionately from the emergence of social media, since they could bypass
traditional media gatekeepers and communicate directly with their respective
target audiences [4]. Now social platform companies increasingly take a role
of being gatekeepers of information or “custodians of the Internet” [5].
Besides, the direct contact to political actors and the represented ideologies
enables the effect of a self-socialisation of citizens and users into right-
wing populist beliefs and worldview [6]. Consequently, social media also
provide an opportunity for top-down claims of leadership for populist parties
and politicians [6].

As stated by the literature, social media have become an important political
forum for the formation of political opinion and consequently also political
persuasion and debate. When mapping these debates it is possible to identify
different groups that arise due to ideologically aligned messaging behaviour
[7, 8]. One strategy used by more or less organised political groups that is
especially relevant for Twitter is the hijacking or ‘hashjacking’ of hashtags
[9, 10, 11, 12, 13]. This notion refers to the function of a hashtag to
represent an issue, movement or organisation and the general expectation that
the stream of information and messages pertaining to these hashtags resembles
in some way the public opinion on that matter [14, 15, 16]. Consequently, the
action of ‘hashjacking’ as using hashtags that were established by politically
opposed groups or a general civil society discourse, is executed with the goal
of quantitatively dominating the content referring to this specific hashtag
[12]. With regards to public hashtag debates, this strategy forms an adverse
group or counter-public that is separated from the wider discourse and forms a
separated cluster [17, 18].

##  3 Hypotheses

Revisiting our investigations on ’hashjacking’ in 2018 [12] and based on the
presented literature on ’hashjacking’ strategies, we formulate the following
hypothesis.

As the share of citizens for which the Internet and social platforms represent
the most important channels for political news consumption is increasing and
political discourse is increasingly taking place on platforms like Twitter, we
assume that debates around the major political parties in Germany remains to
be polarised.

H1): The polarisation of retweet networks of political party hashtags did not
change between 2018 and 2020.

Likewise, we assume that civil society discourse on Twitter relating to issues
like the containment of the COVID-19 pandemic is subject to polarisation.

H2): Retweet networks of COVID-19 related hashtags show a significant degree
of polarisation.

As alternative communication channels (social platforms and messengers) are
particularly attractive for right-wing populist and radical-right politicians,
we assume that COVID-19 related hashtags, like #CoronaVirusDE and
#FlattenTheCurve, have been systematically targeted by German far-right
partisans of #AFD.

H3): There is a significant activity of right-wing partisan accounts in
contra-clusters of COVID-19 retweet networks.

##  4 Research Design

The study is based on Twitter data that was collected by accessing Twitter’s
Rest API and using political party hashtags of German parties represented in
the federal parliament (#AFD, #CDU, #CSU, #FDP, #GRUENE, #LINKE, #SPD) and
COVID-19 related hashtags (#CoronaVirusDE and #FlattenTheCurve) as a macro-
level selection criterion. In total this study builds on a sample (n=101,765)
of all public accounts using one or multiple of the selected political party
hashtags between May 28th and June 4th in 2018 and 2020 on Twitter. The
analysis focuses on a network approach and a visualisation of the retweet
networks in Gephi using the Force2 layout algorithm [19] for each hashtag
where retweeting creates a link (edge) between two accounts (nodes). Since the
data was collected as separate streams of data pertaining to each hashtag, an
account using several hashtags during the observation period in a retweet or
being retweeted, will appear in each of the respective hashtag networks.

Political discourses on Twitter show polarised or clustered structures due to
the retweeting behaviour, as indicated by the literature. The analysis,
consequently, focuses on the retweeting networks of the chosen hashtags. In a
first step of analysis the modularity (community detection) algorithm [20]
assigns the nodes to different communities based on the structural properties
of the network graph. The cluster membership is indicated by the colour of
nodes in Figure 2. Thereafter, the interpretability of the clustering, as
being in support of or opposition to a party, is controlled with a qualitative
content analysis of the 50 most retweeted accounts similar to Neuendorf et al.
[21]. This pro-/contra-polarisation of each party retweet network allows us to
assign accounts as partisans of a specific party hashtag.

Based on previous invesitagations [12], we know that a high pro-party X &
contra-party Y association indicates ’hashjacking’ strategies. Consequently,
we use a logistic regression model to test all cluster combinations (as the
likelihood to be in a contra-cluster of hashtag Y given a node was in the
partisan cluster of party X). We decide to apply a logistic model for the
assessment of cross-cluster heterophily, since our dependent variable is
binary (location in contra-cluster) and the resulting odds are easy to
interpret. Assuming there is no group that uses other party hashtags more
frequently, users from all clusters should have the same odds to appear in the
other network clusters. Thus, a high affiliation between two clusters in terms
of their users being more likely to appear in both of them is an indication
for strategic hashtag use or ’hashjacking’.

##  5 Results

In the first part of the analysis, we compare the polarisation of German
political party hashtags, i.e., the share tweets in either a pro and contra-
cluster, between our data sample collected 2018 and 2020. As illustrated in
Figure 1, we observe that the share of polarised tweets did not change much
between 2018 and 2020, thus confirming research hypothesis 1). Polarisation,
in our definition, is still among highest for #AFD, while the polarisation,
i.e., growth of the contra-cluster, has likewise increased for the hashtag of
the Socialdemocrats (#SPD).

Figure 1: In comparison to our data collected in May 2018, polarisation (the
share of pro versus contra tweets) of the seven major German party hashtags
has not changed significantly. It is still among highest for #AFD, while the
polarisation (growth of the contra-cluster) has likewise increased for the
hashtag of the Socialdemocrats (#SPD)

Secondly, similar to party hashtags, we construct retweeting networks for two
COVID-19 related hashtags most popular in Germany in May 2020: #CoronaVirusDE
and #FlattenTheCurve. With the example of #CoronaVirusDE, Figure 2 illustrates
the shape of these networks. After the application of a Louvain clustering
[22] and modularity (community detection) algorithm, at least two different
clusters emerge. Accounts coloured in dark blue in the bottom left of the
network are centred around the account of @tagesschau, Germany’s major public-
service national and international television news service. Similar to the
accounts in the centre of the network (in light blue colours), users retweet
news and governmental updates on COVID-19 in Germany. However, accounts the
top-right cluster (in orange) have mainly shared tweets in which
#CoronaVirusDE had been used for questioning the existence of COVID-19 or the
necessity of measures taken against the virus. Many of these accounts are
partisans of #AFD, as indicated in red. The strong clustering of the
#CoronaVirusDE retweet network (same holds for #FlattenTheCurve) indicates a
confirmation of hypothesis 2); retweet networks of COVID-19 related hashtags
show a polarisation of users.

Figure 2: In April 2020, users of the hashtag #CoronaVirusDE are clearly
clustered. Users top-right clusters (orange) have shared tweets questioning
the existence of COVID-19 or the necessity of measures taken against the
virus. 42 percent of these users and 69 of the 100 most active retweeters are
partisans of the hashtag #AFD (highlighted in red). We estimate the odds that
a user retweeting #CoronaVirusDE in critical tweets is a #AFD partisan:
$Odds(X\;\epsilon\;Contra\;\\#CoronaVirusDE\;|\;X\;\epsilon\;\\#AFD\;partisan)$.

(A) (B)

Figure 3: (A) For the retweet networks of #CoronaVirusDE and #FlattenTheCurve,
partisans of #AFD are five times, respectively 1.5 times more likely to appear
in a contra-cluster than a user chosen at random. Partisans of other major
Germany political party hashtags are less likely to occur in a contra-cluster.
(B) The group of #AFD partisans contains a set of heavy users. 39 percent of
all #AFD partisan retweets in our sample refer to one percent of #AFD partisan
accounts. Similarly, 50 percent of all retweets stem from the ten percent most
active users.

Based on the distribution of users in the retweet accounts of #CoronaVirusDE
and #FlattenTheCurve we calculate conditional odds of ’hashjacking’, i.e., the
likeliness that an account in the contra-cluster of a given COVID-19 hashtag
is part of a given partisan network, as shown in Figure 3A). Here, we see that
partisans of #AFD are five times more likely to occur in the contra-cluster of
#CoronaVirusDE and 1.5 times more likely to be part of the contra-cluster of
#FlattenTheCurve than a user chosen at random. On the contrary, partisans of
other party hashtags are less likely to appear in either of the contra-
clusters. This finding delivers evidence confirming research hypothesis 3); a
significant ’hashjacking’ activity of right-wing partisans, which is higher
than for partisan groups of other political party hashtags.

Lastly, we examine the activity distribution of individual partisan accounts.
Figure 3B) shows accounts across partisans by their activity of retweeting and
being retweeted. For all partisan groups, we see a large share of users with
very little activity (less than one retweet). However, the distribution of
#AFD partisans appears to be extremely long-tailed; it contains a set of heavy
users. In fact, 39 percent of all #AFD partisan retweets in our sample
reference one percent of all #AFD partisan accounts. Similarly, 50 percent of
all retweets stem from ten percent of the most active users. The activity
(retweeting) of far-right partisans is higher than for partisan groups of
other political party hashtags.

Many of these heavy users contribute to the polarisation of COVID-19 related
hashtags, like #CoronaVirusDE. The #AFD partisan accounts in the network in
Figure 2 are highlighted in red. Their dominance in the sceptic contra-cluster
(orange) is clearly visible. 42 percent of all users in the #CoronaVirusDE
contra-cluster are #AFD partisans. In fact, 69 of the 100 most active users
(in terms of retweets) in this cluster are partisans of #AFD.

##  6 Conclusion

In this study we investigate the discourse polarisation on Twitter as a result
of far-right partisans’ application of a hashjacking strategy. For the case of
COVID-19 related hashtags and German major political party hashtags, we find
that partisans of the “Alternative für Deutschland” (#AFD), systematically
polarised discussions on #CoronaVirusDE or #FlattenTheCurve. Similarly, we
find that polarisation on political party hashtags in Germany did not change
much between 2018 and 2020. But for the exception of the Socialdemocrats’
hashtag (#SPD), with a growing opposition cluster, polarisation remains
highest for the far-right. #AFD partisans are much more likely to engage in
retweeting on COVID-19 related hashtags questioning the existence of COVID-19
or the necessity of measures taken against the virus. At the same time, we
highlight that the activity of far-right partisans is much driven by a handful
of very active users. 50 percent of all retweets among #AFD partisans stems
from the ten percent most active accounts.

Our work validates the assumption of a high polarisation of debates related to
COVID-19. Furthermore, we find evidence that this polarisation is
strategically driven by partisans of the far-right. It takes only a small set
of very active accounts to polarise retweeting debates on hashtags such as
#CoronaVirusDE or #FlattenTheCurve. For users, it is important to be aware of
these dynamics of social media discourse, when engaging in it. For platform
providers, we show that the critical monitoring of a selected set of accounts
can help to contain the spread of misinformation at large.

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