Network Gatekeeping on Twitter During the German National Election Campaign 2017
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Extended Abstract für das 14. Düsseldorfer Forum Politische Kommunikation vom 5. – 7. April 2018 Network Gatekeeping on Twitter During the German National Election Campaign 2017 A Social Network Analysis of the Social Democratic Party’s Parliamentary Group’s Twitter Network – Between Normalization and Democratization Susanne Reinhardt Institut für Publizistik und Kommunikationswissenschaft Freie Universität Berlin s.reinhardt@fu-berlin.de 05.02.2018
Network Gatekeeping on Twitter During
the German National Election Campaign
2017
A Social Network Analysis of the Social Democratic Party’s Parliamentary Group’s
Twitter Network – Between Normalization and Democratization
The availability of the internet and nowadays social media to increasing portions of the world’s
population has nurtured various conceptions of how these new technologies could affect established
structures of states, governments and decision-making.
Aspirations of democratization can be summed up to more egalitarian access to information,
distribution capacities and reciprocal communication with elites as well as to disintermediation and
bottom-up agenda-setting (Neuberger, 2017; Pfetsch & Adam, 2013). In contrast, the normalization
thesis states that power structures existing offline are mirrored online and that offline elites also
dominate online public spaces (Margolis & Resnick, 2000).
To understand which actors dominate online public spaces and control information flows,
gatekeeping theory is introduced. Adaptations of gatekeeping theory to a social network context
suggest changes in the gatekeeping process that imply the integration of a greater quantity of actors
into the process. The main structural disruption is that in the network structure of social media, the
hierarchization of information functions different than in offline media. Content has to pass a second
gate that is based on audience interaction with content to gain publicity (Keyling, 2017; Lünich,
Rössler, & Hautzer, 2012; Shoemaker & Vos, 2009; Singer, 2014).
This bears a potential to change the constellation of actors participating in the gatekeeping process.
Thus, the first question raised by this research is: Do changes in the gatekeeping process effect a
democratization of the constellation of actors involved in the gatekeeping process? Democratization
of actors is assumed to occur when a higher diversity of actors and a more balanced proportion of
elites and non-elites can be found among the online gatekeepers.
Based on this research interest, election campaign communication in online social networks is
analyzed because political communication by parties is highly distributive during election campaigns
(Borucki, 2016; Nuernbergk & Conrad, 2016) and campaign managers aim to reach influential
individuals that interact with their content to increase its reach (Jungherr, 2016; Podschuweit &
Haßler, 2015).
Empirical evidence suggests that in online campaigning, political information largely reaches those
publics that are politically interested or belong to a party’s voter base (Faas & Partheymüller, 2011).
1Thus, in social media, political parties cannot rely (only) on traditional mass media anymore to spread
their messages. Consequently, new strategies to distribute content must be employed. The second
research interest is therefore: Besides media, through which other actors can political parties
distribute their contents on social media?
Network theory is used as a theoretical foundation of information flows and structural hierarchies in
a networked context. Small World Theory (Watts & Strogatz, 1998) suggests that networks are highly
centralized, which causes some actors to be more relevant than others.
Data is collected in the network of the social democratic party’s parliamentary group @spdbt during
the German national elections in August 2017. NodeXL pro (Smith et al., 2010) is used to collect
network data from Twitters’ API. A Social Network Analysis (SNA) is conducted to detect central
actors.
Network gatekeepers are defined to be those actors capable to bridge information and those that
are highly visible and influential in the network. Thus, betweenness centrality and eigenvector
centrality are suitable measures to identify network gatekeepers. Gephi (Bastian, Heymann, &
Jacomy, 2009) is used to visualize the network. The 305 most central actors are also subject to an
actor analysis, including the following variables: Actor Category, Verification Status,
Individual/Organization, Sex, Affiliation to SPD and Elite/Non-Elite.
Each of the subgroups of network gatekeepers differs from the others significantly in some features
(see Table 1). The group of actors in bridging positions (n = 223) is dominated by political actors (43.5
%) and citizens (34.1 %), while media make up only 8.1 % of the group. Actors in this group are
mostly unverified (70.0 %) and male (54.7 %). There is a balance between elites (57.4 %) and non-
elites (43.6 %).
Influential network positions are dominated by elites (80.1 %). The group of actors (n = 151) consists
of 61.6 % political actors and 21.9 % media actors, while all other categories remain small. Influential
actors are mainly verified (63.6 %) and male (48.3 %).
Multiplicators (n = 28), which means actors that retweeted @spdbt’s messages, are mainly citizens
(60.7 %) or political actors (28.6 %). They are mostly unverified (82.1 %), male (60.7 %) and non-elites
(67.9 %).
Comparing basic network metrics among the actor categories it becomes obvious that political actors
and elites have a high average clustering coefficient and show high average in-degrees, while having
comparatively low out-degrees. A high in-degree is not always connected to a high message volume
(see Table 2).
The network shows both indicators of democratization and normalization. Democratization is
suggested by the low relevance of media in bridging positions and the multiplication of messages,
2which point to disintermediation. A growing importance of citizens is indicated by their high
presence in bridging positions and their importance as multiplicators. However, the democratic
potential of these changes depends on the distribution capacity and thus the influence that citizens
can reach.
Influential network positions are mainly occupied by elites. Elites tend to build cliques among each
other – the data suggest interconnections between the ‘politics’ and ‘media’ category. The
distribution of in- and out-degree show clear attention hierarchies in favor of political actors and cast
doubts on the existence of reciprocal communication between elites and non-elites. While citizens
occupy bridging positions and act as multiplicators, the democratic potentials of these changes do
not fully unfold because influential network positions are still occupied by elites, and there is a lack of
reciprocity.
Coming back to the two research interests pointed out in the introduction, firstly, this means that in
the analyzed network, democratic potentials exist within normalized structures. Citizens still hardly
reach elites, and elites remain the most influential in the online public sphere, but citizens reach each
other more easily and can distribute topics of relevance to them. This can nurture opportunities for
bottom-up agenda-setting.
Secondly, citizens become active as network gatekeepers and must be included in parties’
communication strategies not just as an audience but as multiplicators. This also influences the
nature of gatekeeping as the factors influencing professional gatekeeping are expected to differ from
those factors influencing citizens’ gatekeeping activities. Gatekeeping outside a professional context
can have both positive and negative outcomes – it indicates an increasing influence of citizens in
online public spheres but can also nurture the distribution of contents harmful to a democracy.
(1.090 words)
3References
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4Annex: Data
Table 1: Portions of Categories by Gatekeeper-Subgroups in Percent
Network ‘Betweenness’- ‘Eigenvector’-
Gatkeepers Gatekeepers Gatekeepers Multiplicators
(n = 305) (n = 223) (n = 151) (n = 28)
Media 14.8 8.1 21.9 3.6
Politics 46.2 43.5 61.6 28.6
NGOs 6.9 7.2 6 3.6
Companies 1.5 2.2 0.7 -
Research 2.3 1.8 2 -
Entertainment 2 2.7 0.7 3.6
Bloggers 0.7 0.4 0.7 -
Citizens 25.6 34.1 6.6 60.7
Unverified 58.4 70 36.4 82.1
Verified 41.6 30 63.6 17.9
Individual 77 82.1 71.5 89.3
Organization 23 17.9 28.5 10.7
Male 51.1 54.7 48.3 60.7
Female 22.6 22.9 22.5 25
Neutral 26.2 22.4 29.1 14.3
No Affiliation 55.1 51.6 47 57.1
Affiliation 44.9 48.4 53 42.9
Non-Elite 42.6 47.1 19.9 67.9
Elite 57.4 52.9 80.1 32.1
Table 1: Portions of Categories by Gatekeeper-Subgroups in percent. All percentages are rounded to
one decimal place.
iTable 2: Descriptive Statistics by Category
Media Politics NGOs Companies Research
In-Degree 20.4 241.5 19.5 4 4.4
Out-Degree 7.9 33.7 37.9 4 2.3
Clustering Coefficient 0.38 0.49 0.34 0.35 0.47
Followed 2230 988 5550 5870 812
Followers 184807 32334 19274 8796 4902
Tweets 27150 5938 5124 1936 5542
Entertainment Bloggers Citizens Elite Non-Elite
In-Degree 31.3 5.5 60.5 199.4 42
Out-Degree 20.7 5 87.7 28.8 61
Clustering Coefficient 0.07 0.26 0.23 0.46 0.28
Followed 31540 3213 9970 1272 8785
Followers 31743 199183 17356 73094 19305
Tweets 22131 18747 34600 50167 23853
Verified Unverified Male Female
In-Degree 483.7 46.3 143.1 55.8
Out-Degree 38.3 65 36.4 67.6
Clustering Coefficient 0.46 0.31 0.44 0.41
Followed 2191 7429 3578 4800
Followers 37566 12348 22620 15238
Tweets 11372 19083 14500 14931
Table 2: Descriptive Statistics by Category. In-Degree and Out-Degree are rounded to one decimal
place. Clustering coefficient is rounded to two decimal places. Followed, Followers and Tweets are
rounded to whole numbers.
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