# Election campaigning on social media: Politicians, audiences and the
mediation of political communication on Facebook and Twitter

Sebastian Stier, Arnim Bleier, Haiko Lietz, Markus Strohmaier
_GESIS – Leibniz Institute for the Social Sciences_
<EMAIL_ADDRESS>
###### Abstract

Although considerable research has concentrated on online campaigning, it is
still unclear how politicians use different social media platforms in
political communication. Focusing on the German federal election campaign
2013, this article investigates whether election candidates address the topics
most important to the mass audience and to which extent their communication is
shaped by the characteristics of Facebook and Twitter. Based on open-ended
responses from a representative survey conducted during the election campaign,
we train a human-interpretable Bayesian language model to identify political
topics. Applying the model to social media messages of candidates and their
direct audiences, we find that both prioritize different topics than the mass
audience. The analysis also shows that politicians use Facebook and Twitter
for different purposes. We relate the various findings to the mediation of
political communication on social media induced by the particular
characteristics of audiences and sociotechnical environments.
Keywords: online campaigning; social media; cross-media analysis; text
analysis; language models
Forthcoming in _Political Communication_
DOI: https://doi.org/10.1080/10584609.2017.1334728

## Introduction

Social media have become ubiquitous communication channels for candidates
during election campaigns. Platforms like Facebook and Twitter enable
candidates to directly reach out to voters, mobilize supporters and influence
the public agenda. These fundamental changes in political communication
therefore present election candidates with a widened range of strategic
choices. Should candidates address the topics most important to a mass
audience? Should they tailor their messages to the specific habits and
audiences on social media platforms? Although academic research on social
media campaigning has flourished in the last several years (Jungherr, 20162;
Boulianne, 2016), it is still unclear which topics politicians address on
these platforms, since previous research mostly concentrated on meta data
generated by the use of communication conventions such as retweets,
@-mentions, likes or hashtags. Understanding the ways in which politicians
adapt the contents of their messages to the peculiarities of different
platforms generates deeper insights into how political communication is shaped
by social media.

Much research revealed a continuation of the status quo in online campaigning,
as politicians mostly replicated traditional messages and campaign modes on
their web presences while limiting engagement with users (Stromer-Galley,
2000; Lilleker ., 2011; Gibson ., 2014; Larsson, 2015). New media
notwithstanding, it seems imperative for candidates to tailor their online
messages towards the preferences of the majority of voters, in line with
models of mass communication (Downs, 1957; Druckman ., 2010). However, the
observation of McQuail (2010, p. 140) that “the audience member is no longer
really part of a mass, but is either a member of a self-chosen network or
special public or an individual” is especially true for interactive social
media. On these platforms, politicians get directly exposed to users with
rather specific demographic characteristics and political interests (Diaz .,
2016; Nielsen Vaccari, 2013; Schoen ., 2013) and have to adapt to unique
affordances of social media sites (Hoffmann Suphan, 2017; Jungherr ., 2016).
Candidates might therefore tailor their communication to the sociotechnical
environments of platforms like Facebook and Twitter. To infer if campaign
messages are aimed at a mass audience or more particular sets of audiences, we
analyze “the distribution of salience across a set of issues” (Iyengar, 1979,
p. 396) and make a novel contribution by (1) deriving these distributions from
survey responses to proxy the preferences of a mass audience, as well as (2)
from different social media and (3) from multiple content layers (politicians
and audiences on social media).

The empirical analysis focuses on political communication on Facebook and
Twitter by candidates during the German federal election (_Bundestagswahl_)
campaign 2013. The baseline is a representative survey of the German
population conducted during the election campaign in which participants were
asked to describe in free text the most important contemporary political
problem. These responses were assigned to 18 topic categories by the survey
administrators. We present a human interpretable Bayesian language model which
allocates social media messages to the known topic categories from the survey
responses, but creates additional social media specific topics, if necessary.
Applying the model to social media messages of candidates and their social
media audiences, we find that although their topic agendas converge to some
extent with the survey, both prioritize topics like campaign-related events
that are different from the concerns of a mass audience. Furthermore, the
focus of candidates on social media is more similar to the topics discussed by
the audiences to which they are most directly exposed. An analysis of the
language used in messages adds methodological robustness to the results and
confirms that politicians primarily use Facebook for campaign-related purposes
like the promotion of their activities while preferring Twitter to comment on
contemporary political events. We relate these differences to the mediation of
political communication on social media induced by diverging characteristics
of audiences and sociotechnical environments.

## Related literature and research gaps

Our study is located at the intersection of cross-media and social media
research. There is an established research tradition relating the use of
different media to outcomes and processes like political knowledge,
participation and voting (Prior, 2007), news consumption (Althaus Tewksbury,
2002) and political communication (Druckman ., 2010).

In terms of election campaigning on the Internet, Druckman . (2010) presented
several relevant findings for our study. The campaign officials the authors
surveyed revealed that even though they were aware that supporters are the
most frequent visitors of candidate websites, these formats were still
designed for a mass audience. In a comparison of websites and TV ads, the
authors showed that candidates are equally likely to use both media for
negative campaigning, implying that the medium and different user groups do
not matter much in campaign strategy. Other studies mirrored the rather
conservative use of the web by politicians (e.g. Stromer-Galley, 2000;
Lilleker ., 2011; Gibson ., 2014; Larsson, 2015).

In terms of audience behavior, Althaus Tewksbury (2002) established that
whether news are consumed offline or online affects perceptions of issue
importance. The selection of news at the individual level is “filtered” by
different media, even when the original source is the same. While the
newspaper edition of the New York Times heavily guides the readers by
providing journalistic cues and a steady diet of “public affairs coverage”,
users of its online edition were navigating contents in a manner that suits
their personal preferences. Others have observed an audience fragmentation and
turn towards entertainment formats in high-choice media environments as well
(e.g. Prior, 2007; Nielsen Vaccari, 2013).

Taken together, these findings posit that although the citizens who actually
use the Internet for political purposes have rather specific political
interests, politicians have so far used the web with a mass audience in mind.
The skewed perceptions of topic importance by Internet users that Althaus
Tewksbury (2002) observed should be even more pronounced when using social
media, given the social and algorithmic cues that these platforms provide.
Accordingly, Jungherr . (2016) showed that during the German federal election
campaign 2013, topic priorities of Twitter audiences deviated from a survey
and mass media coverage. Strategic campaigns should adapt to these
environments and narrowcast their messages to the particular audiences they
encounter (Kreiss, 2016).

Election campaigning on social media has been studied extensively, as
researchers examined how election campaigns unfold, how candidates are
embedded in communication networks and how they interact among themselves and
with the public (cf. Jungherr, 20162; Boulianne, 2016). Still, in terms of
cross-media research, this literature is limited in several regards: first,
most studies focused on one isolated platform, overwhelmingly Twitter and –
less often – Facebook. Second, only a fraction of this work concentrated on
the actual contents of communication going beyond meta data, i.e., digital
traces left by communication artifacts like @-messages, retweets, likes or
hashtags. While several studies coded contents of social media posts by U.S.
parliamentarians (Golbeck ., 2010; Gainous Wagner, 2014; Bronstein, 2013),
these efforts mostly consisted of smaller samples and/or did not specifically
categorize the topics politicians talk about. Third, most research is confined
to the boundaries of election campaigning on a given social media platform.
The few cross-platform analyses either restricted themselves to main accounts
of party organizations (Rossi Orefice, 2016; Larsson, 2015) or metrics of
attention like the number of views or followers on multiple platforms (Nielsen
Vaccari, 2013). In recent advances, Karlsen Enjolras (2016) linked a candidate
survey with Twitter data to uncover candidates’ strategies and determinants of
Twitter success. Bode . (2016) compared television advertisements with Twitter
data and identified deviations indicating that the two media represent
distinct modes of campaigning. Building on that, a comparison between multiple
social media platforms might reveal even more fine-grained affordances of
different media. Such platform specific mediation effects urgently need to be
taken into account in models of political communication (Jungherr ., 2016).

We add to this research by integrating features from multiple spheres of
political communication in one research design. Recent advances in
quantitative text analysis (Grimmer Stewart, 2013) enable us to perform text
analysis at a larger scale. We regard responses to an open-ended question in a
representative survey as reflections of the topic priorities of a mass
audience and take these as an empirical base for the analysis of social media
messages by candidates and their audiences.

## Strategic election campaigning on social media

Politicians seeking election need to be responsive to the political
preferences of their constituencies (Downs, 1957). However, it is an open
question if politicians tailor their online messages to the topic priorities
of a mass audience or particular social media audiences. In contrast to
Druckman . (2010) who revealed rather traditional strategies on campaign
websites, we argue that social media poses a yet again different communication
constellation: politicians are embedded in an interactive context which skews
their messages to the topic preferences of their immediate communication
network (see also Bode ., 2016). This might be due to strategic reasoning in
order to increase the success of messages or an unwitting outcome of the uses
and gratifications of politicians themselves.

Our empirical study relies on survey and social media generated during the
German federal election campaign 2013. Given the German electoral system where
party identification is still rather strong (Arzheimer, 2006), district-level
topics are of minor importance in election campaigns and public agendas mostly
converge to the topics salient at the national level. Therefore, topic
salience expressed in politicians’ social media messages can reasonably be
compared to topic salience in public opinion polls. To the best of our
knowledge, no such research has been undertaken so far, which naturally makes
our study an exploratory one. Yet we derive useful propositions from previous
research to develop several theoretical expectations.

### Social media as part of multifunctional online campaigns

When theorizing on the topics of politicians’ campaign messages, at least
three arguments indicate that social media indeed pose campaign environments
distinct from mass communication arenas. First, we have to consider that
social media are not only used to address political topics important to a mass
audience, but perform several other functions in election campaigns. Kobayashi
Ichifuji (2015), for instance, identified three functions: promoting issue
positions, demonstrating beneficial personality traits and improving name
recognition. Jungherr (20161) proposed a four-fold typology distinguishing
between organizational uses, active campaigning in information spaces,
resource collection and allocation as well as symbolic purposes. A
considerable share of online campaigning should therefore be devoted to the
mobilization of supporters, organization of campaigns (Lilleker ., 2011;
Nielsen Vaccari, 2013) and representational/symbolic purposes. Contrary to the
“electronic brochures” of websites (Stromer-Galley, 2000; Hoffmann Suphan,
2017), a campaign has to internalize a whole set of platform-specific
affordances on social media in order to demonstrate that it represents the
“state of the art”.

Second, the demographic composition as well as the political preferences and
interests of social media audiences are much different from a representative
sample of citizens (Diaz ., 2016; Schoen ., 2013). In the context of the
German election 2013, political audiences on Twitter rarely talked about core
political issues like the Euro crisis or energy policy, but predominantly
addressed NSA surveillance and campaign-related events like the televised
debate between the leading national candidates (Jungherr ., 2016). Strategic,
“micro-targeted” (Kreiss, 2016) campaigns should tailor their messages to
specific audiences and successful marketing strategies on social media. While
the German political system and privacy regulations certainly pose barriers to
data-driven micro-targeting (Stier, 2015), mediation effects of social media
platforms should still be felt in online campaigning. In consequence,
candidates might be inclined to address topics like internet policy, which are
more important to social media than mass audiences, and unfolding campaign
events.

Third, candidates themselves have different uses and gratifications of online
media (Hoffmann Suphan, 2017; Marcinkowski Metag, 2014). One of the central
features of the Internet is its coalescence of private and public functions
(McQuail, 2010, p. 41), and many German politicians indeed operate their
Twitter accounts personally (Spiegel Online, 2015). Studying individual-level
predictors of online campaigning, Marcinkowski Metag (2014) revealed that
German state-level candidates with a more positive attitude towards the
Internet use its applications more intensively. Hoffmann Suphan (2017)
reported that although the motives self-promotion and information
dissemination are dominant among Swiss politicians, which is consistent with
traditional modes of campaigning, they also use social media for more personal
uses like information seeking and entertainment. The authors conclude that
“specific motives—possibly based on varying levels of understanding of and
experience with new media—can lead to more or less avid and strategic ICT
adoption” (Hoffmann Suphan, 2017, p. 251).

Given the multiple functions of campaigns as well as an adoption of typical
practices and engagement with specific audiences by a considerable share of
candidates, it can be assumed that the topics salient in politicians’ social
media messages do not necessarily reflect the topic priorities of a mass
audience.

H1: Topic saliences in messages by candidates and audiences on social media
are more similar to each other than to topic salience among a mass audience.

### The use of Facebook and Twitter by election candidates

Due to the different architectures of social media platforms, topic saliences
could also differ between the two networks we look at, Facebook and Twitter.
Previous studies have shown that different media logics influence the
strategic considerations underlying election campaigns (Kreiss, 2016; Bode .,
2016). On Twitter, most user accounts are publicly visible and accessible even
for non-registered audiences. Its usage is centered around topics and the
retweet feature facilitates the diffusion of political information beyond the
direct follower network via two-step flow processes (Wu ., 2011). In contrast,
most accounts on Facebook are private and its usage is based on one-way or
reciprocal friendship ties. Information travels less fluidly through this
medium, also due to the extensive algorithmic filtering of contents. Thus, the
audience for Facebook posts mostly consists of people already “liking” a
candidate page (see for similar audience-centered arguments: Norris, 2003;
Nielsen Vaccari, 2013).

Among the most active Twitter users are prime targets of campaigns like
political elites and influentials. Journalists, for instance, regard Twitter
of higher value for news reporting while using Facebook primarily for private
purposes (Parmelee, 2014). Campaign officials interviewed by Kreiss (2016)
emphasized that journalists use Twitter as an “index of public opinion”, which
implies that targeted campaign messages on Twitter had the potential to create
spillover effects to other media. Similarly, Twitter is used more intensively
by Swedish political parties than Facebook due to the greater potential to
reach opinion leaders via the former medium (Larsson, 2015). Disaggregating
campaigning by political arena, Larsson Skogerbø (2016) provided evidence from
Norway that local politicians valued Facebook higher for political
communication, whereas politicians at the national level preferred Twitter.
The former medium seems to be particularly suited for local politics, which
election campaigning is to a large extent about, while the latter is being
used to connect to national audiences.

Considering these findings, we expect that candidates use Twitter to
contribute to contemporary national debates about policies or high-attention
campaign events like TV debates. In contrast, politicians “preach to the
converted” on Facebook (using the words of Norris, 2003), mostly party
supporters and local constituents. Since these users already show a
considerable interest in a candidate and her political stances, the strategic
value of this friendly audience lies in mobilization, i.e., persuading
followers to take part in the election campaign as volunteers or turn out to
vote, rather than in convincing them of policy propositions.

H2: Topic salience among a mass audience is more similar to topic salience by
candidates on Twitter than to topic salience by candidates on Facebook.

H3a: Candidates prefer Twitter to discuss policies and unfolding campaign
events like TV debates.

H3b: Candidates prefer Facebook for campaigning and mobilization purposes.

## Methodology

Language model. We begin with some considerations regarding the joint modeling
of survey responses and social media messages. Classification is a supervised
task of identifying to which group, here topics, a document (a survey
response, Facebook message or tweet) belongs on the basis of labeled training
data. Clustering is an unsupervised task of grouping observations that are
more likely to follow the same word distributions as those in other groups
(Bishop, 2006). Our model occupies a middle ground between classification and
clustering. We use labeled survey responses as training data, but then cluster
those social media messages that use distinct words or word combinations than
survey responses into new topics that capture the specifics of online
communication.

To this end, we introduce a Bayesian semi-supervised nonparametric single-
membership language model. The model allows for the grouping of short messages
into known survey classes and, if necessary, additional social media specific
topics. The underlying assumptions of the model are that documents are
generated in a probabilistic process of (1) drawing a topic for a document and
then (2) drawing the words of that document from the selected topic. Such a
content analysis identifies differences in language as practiced culture and
is therefore particularly suited to distinguish the different communication
styles of survey and social media contexts (McFarland ., 2013).

The model is semi-supervised, allowing for the incorporation of survey
responses coded with a topic label. The labeled documents are responses to an
open-ended survey question on the most important contemporary political
problem in Germany (MIP). Moreover, the model is nonparametric, i.e., we do
not set the total number of topics in advance (Teh Jordan, 2010). Instead, we
use a Dirichlet process prior on the number of topics. While the allocation of
survey responses to topics is known, the allocation of social media messages
to topics is unobserved and has to be inferred. The nonparametric prior allows
for new topics specific to Facebook and Twitter communication to be
established when messages cannot plausibly be explained by the topics used in
the survey. Finally, we follow the single-membership assumption that each
document is generated from a single topic. This assumption handles short
documents such as social media messages or survey responses well (Yin Wang,
2014). In fact, the short length of documents effectively prohibits a mixed-
membership approach, such as the one put forward by Roberts . (2014). A formal
description of this model, specifically tailored to our needs, is given in
Appendix A.

The inference step of the model described in Appendix B reverses assumptions
for document generation to an automated reading of otherwise latent semantic
topics from the social media datasets. For this, we ran the Gibbs sampling
procedure for 100 iterations. For the evaluation of model outputs, the most
important criterion was the substantive fit as determined by human judgment,
i.e., the quality of topics in the context of our research question (Grimmer
Stewart, 2013, p. 286). As parameters, we chose $\alpha=1.0$ and $\beta=1.5$
which produce well-interpretable topics that cluster messages unique to social
media while allocating topically related social media messages to the known
survey topics. The final output provides an aggregated and unified macroscopic
view on signals in political communication that would be impossible to gather
with conventional methods.

GLES survey data. The MIP we use was administered in the Rolling Cross-Section
of the German Longitudinal Election Study (GLES) (Rattinger ., 2014)
accompanying the federal election 2013.11endnote: 1The wording is: “In your
opinion what is the most important political problem facing Germany at the
moment?” (Rattinger ., 2014). The use of such an MIP has been criticized in
terms of conceptualization and measurement in previous research. Some
respondents relate their answer to their individual situation, some to the
political situation at the societal level, and some consider the question
based on their voting considerations (Wlezien, 2005). People therefore have
different understandings of topic importance, of the extent to which a topic
should be considered as a problem and also of the perspective that should be
taken when making these judgments. These methodological considerations
notwithstanding, our application of the MIP is less problematic than its
predominant application in survey research, in which open-ended responses are
reduced to one quantitative scale of issue salience. In contrast, we take the
entire textual contents of each response into account and therefore capture
all the semantic facets related to the _importance_ of topics and citizens’
perception of them as being _problematic_ (arguing similarly: Mellon, 2014).
Therefore, the MIP question serves our methodology well as a “catch all
category” soaking up the nuances in the vocabulary used by the respondents,
which will in turn also be reflected in the inference stage when the model is
applied to social media messages.

The GLES survey was in the field from 8 July until 3 November 2013, surveying
7,882 unique participants twice, in a pre-election and a post-election wave.
We pooled all responses on the first and second MIP during this time period,
which gives us a total of 23,604 observations.22endnote: 2Before the election,
7,249 participants (92%) stated a MIP and 6,673 (85%) stated a second MIP.
After the election, 5,016 respondents (64%) stated a MIP and 4,666 (59%)
stated a second MIP. Pooling implicates that observations are not independent
of each other, since most participants responded multiple times in the
survey.33endnote: 34,220 (54%) stated four problems, 543 (7%) stated three,
2,364 (30%) stated two and 367 (5%) stated one problem. 388 (5%) did not know
a problem or did not respond. Thus, the sample loses its initial
representativeness when we aggregate responses to GLES topic categories for
our empirical analysis. Yet the practical benefits of pooling to construct a
sufficiently large training dataset outweigh its theoretical disadvantages,
since we primarily use the survey responses for the extraction of textual
information.

The open-ended responses were coded by GLES experts according to a
hierarchical categorization of topics (GLES, 2013) which consists of the three
traditional higher-level dimensions used in political science (politics,
polity and policy). 188 responses (0.8%) were concerned with political
processes (politics) while 1,063 observations (4.5%) were concerned with
political structures (polity). The overwhelming share of survey responses thus
located the most important problem in a policy area. The classes for these
responses required additional manual filtering and reassembling in order to
arrive at discriminative and more equally sized training classes. As a result,
we obtained 18 topics for training (see Appendix C).

Social media data. We use a dataset covering political communication on
Facebook and Twitter during the election campaign for the German Bundestag
2013 gathered via the Facebook Graph API and the Twitter Streaming API
(Kaczmirek ., 2013). From this dataset, we extracted all social media messages
posted by candidates, their incoming @-mentions on Twitter and the comments to
their posts on Facebook during the research period analogous to the
survey.44endnote: 4The data mining of Facebook posts ended three days earlier,
on 31 October 2013. We only selected posts by parties with a realistic chance
of passing the electoral threshold of 5% required for representation in the
Bundestag, namely CDU, CSU, FDP, Grüne, Linkspartei and SPD. The AfD almost
made it into the Bundestag in 2013, however, its rise to prominence in public
opinion polls came late and thus the party was not incorporated in the data
collection.

The dataset contains 49,573 Facebook posts and 134,462 tweets by candidates.
As audience data, we preselected all 180,214 comments on candidates’ Facebook
posts and all 282,118 tweets that @-mentioned at least one candidate. These
numbers at first seem to reflect a low interest by the public. However, it
should be kept in mind that election-related topics are also discussed without
commenting on candidates’ Facebook posts or mentioning them in tweets. We
chose these two particular metrics since they are the most direct exposure of
a candidate to messages by her audience (account holders per default get a
notification via the platform interface and via email that they received a
mention/comment).

Preprocessing. We preprocessed our five corpora (survey, politicians Facebook,
politicians Twitter, audience Facebook, audience Twitter) before we applied
our model, which is a necessary reduction of linguistic complexity for text
analysis (Grimmer Stewart, 2013). The preprocessing of documents involved
removing punctuation, standard German stop words, URLs and Twitter user
handles. In addition, we removed the names of all political parties and
candidates to prevent inferring topics that are party-specific or centered
around social relations. Customized stop words, compiled inductively while
refining the model, were also removed. This customized stop word list contains
ambivalent words with high probabilities in many distinct topics (e.g.,
“Politik”) as well as very frequent and very infrequent words. Finally, only
those social media messages were kept that contain at least three words in
order to incorporate sufficient information for the clustering procedure of
the model. Survey responses were allowed to consist of only one word since the
topic was already known for these documents.

In addition to text cleaning, we made sure that the four social media corpora
are comparable in size and number of words used so that they have a comparable
influence on topic construction. To achieve this, we operationalized the
Facebook and Twitter audience corpora as random samples of equal size to
politicians’ messages, stratified by political party. For instance, the 5,902
tweets by FDP candidates were mirrored by the same number of audience tweets
mentioning an FDP candidate and their 1,777 Facebook posts were matched by as
many randomly sampled accompanying Facebook comments.55endnote: 5We took
audience tweets in which only one politician was mentioned for the sampling.
In total, 22,186 survey responses, 17,546 Facebook posts by politicians,
17,546 Facebook comments, as well as 54,093 tweets by politicians and 54,093
@-mentions were used as inputs for the model.

## Results and discussion

### Analysis at the document level

Figure 1: Social media messages over time.

We start our analysis with a description of social media activity by
politicians and audiences over time using the raw counts of messages from the
unpreprocessed dataset. This gives us first indications about specific focus
points and the topics predominantly addressed on social media. The vertical
lines in Figure 1 represent the TV debate between the party leaders Angela
Merkel and Peer Steinbrück (1 September 2013) and election day (22 September
2013). Especially the televised debate drew attention (see also Jungherr .,
2016; Lietz ., 2014), since social media users are particularly active during
these high-attention periods (Diaz ., 2016; Kreiss, 2016). Interestingly, the
Facebook politicians time series barely reacts to the TV debate and not nearly
in the order of magnitude like the other three time series. This indicates
that politicians prefer Twitter over Facebook to comment on unfolding events
(supporting H3a). We will follow up on these hints with an extensive analysis
of the topics discussed by politicians and their audiences on social media.

The text analysis model described above generates 51 topics; the 18 known
topics from the survey and 33 additional ones based on the social media
corpora. We removed 23 new topics with less documents than the smallest known
topic from the survey ($<468$ documents, aggregated across all social media
corpora) in order to improve interpretability. Those 23 residual topics in
total only covered 1,387 of 143,278 social media documents. The first 18
topics were labeled using the known topic labels from GLES (cf. Table A1). The
ten remaining social media topics were labeled by inspecting the documents for
each topic qualitatively. The few ambiguous cases were discussed among the
authors and decided upon consensually.

Table 1: Top words per topic _Known topics from the survey_
---
Budget & Debt: schulden, schuldenabbau, staatsverschuldung, verschuldung,
haushalt, finanzen, ueberschuldung, staatsschulden, abbau, neuverschuldung
_(debt, debt reduction, national debt, debt, budget, finances, debt overload,
national debt, reduction, new debt)_
Currency & Euro: euro, eurokrise, schulden, europa, griechenland, krise, geld,
milliarden, banken, schuldenkrise _(euro, euro crisis, debt, europe, greece,
crisis, money, billions, banks, debt crisis)_
Economy: wirtschaft, finanzkrise, wirtschaftliche, wirtschaftspolitik,
wirtschaftskrise, stabilitaet, wirtschaftlichen, banken, versteht, wachstum
_(economy, financial crisis, economic, economic policy, economic crisis,
stability, economic, banks, understand, growth)_
Education: bildung, bildungspolitik, mitte, schulen, jaehrige, schule, geld,
lehrer, kinder, schueler _(education, education policy, middle, schools,
annual, school, money, teacher, children, pupils)_
Environment: umwelt, umweltschutz, umweltpolitik, wohlstand, klimawandel,
klima, klimaschutz, aufbruch, oekologie, energiewende _(environment,
environmental protection, environmental policy, prosperity, climate change,
climate, climate protection, start, ecology, energy transformation)_
Family Policy: kinder, kita, frauen, betreuungsgeld, familie, familienpolitik,
eltern, familien, gleichstellung, kind _(children, kita, women, child care
subsidy, family, family policy, parents, families, equalization, child)_
Foreign Policy (Defense): syrien, fluechtlinge, krieg, lampedusa, frieden,
russland, aussenpolitik, muessen, europa, waffen _(syria, refugees, war,
lampedusa, peace, russia, foreign policy, must, europe, weapons)_
Foreign Policy (Europe): europa, europapolitik, europaeische, europaeischen,
zusammenhalt, integration, europas, stabilitaet, laender, euro _(europe,
european policy, european, solidarity, integration, europe’s, stability,
countries, euro)_
General Fiscal Policy: finanzen, finanzpolitik, finanzielle, finanzlage,
sicherheit, geld, finanzmarkt, finanzierung, situation, ordnung _(finances,
fiscal policy, financial, financial situation, security, money, capital
market, funding, situation, order)_
General Social Policy: soziale, gerechtigkeit, altersarmut, reich, armut,
schere, ungerechtigkeit, sozialen, wandel, sozialpolitik _(social, justice,
poverty of the elderly, rich, poverty, gap, injustice, social, change, social
policy)_
Health Care & Pensions: renten, rente, pflege, buergerversicherung,
gesundheitspolitik, tvduell, rentenpolitik, gesundheit, gesundheitswesen,
medizin _(pensions, pension, caregiving, citizen insurance, health policy,
tvduell, pension policy, health, health sector, medicine)_
Infrastructure: energiewende, energie, erneuerbare, strom, energien, kohle,
energiepolitik, volksentscheid, umlage, klimaschutz _(energy transformation,
energy, renewable, electricity, energies, coal, energy policy, referendum,
contribution, climate protection)_
Labor Market: mindestlohn, arbeitslosigkeit, arbeit, euro, muessen, steuern,
geld, leben, maut, tvduell _(minimum wage, unemployment, work, euro, must,
taxes, money, live, toll, tvduell)_
Law & Order: nsa, prism, snowden, ueberwachung, datenschutz, affaere, bnd,
freiheit, daten, buerger _(nsa, prism, snowden, surveillance, data protection,
affair, bnd, freedom, data, citizens)_
Migration & Integration: integration, auslaender, auslaenderpolitik,
zuwanderung, migranten, einwanderung, asylbewerber, asylanten, migration,
einwanderungspolitik _(integration, foreigners, policy towards foreigners,
immigration, migrants, asylum seeker, migration, asylum policy)_
Politics: euro, muessen, buerger, jahr, unternehmen, arbeit, stadt, land,
region, leben _(euro, must, citizens, year, businesses, work, city, country,
region, live)_
Polity I: geld, ehrlichkeit, glaubwuerdigkeit, buerger, bevoelkerung,
demokratie, uneinigkeit, ausland, volk, vertrauen _(money, honesty,
credibility, citizens, population, democracy, disagreement, foreign countries,
people, trust)_
Taxes: steuern, steuerpolitik, progression, steuererhoehung, steuer, kalte,
kalten, abbau, steuergerechtigkeit, steuererhoehungen _(taxes, fiscal policy,
progression, tax increase, tax, cold, reduction, fiscal justice, tax
increases)_
_New topics found on social media_
Campaigning (Events): veranstaltung, talk, podiumsdiskussion, wahlkreis, gast,
einladung, interview, unterwegs, bundestagswahl, gespraeche _(event,
conversation, panel discussion, constituency, guest, invitation, interview, on
the road, federal election, talks)_
Campaigning (Local): danke, dank, super, guten, stimmung, spass, infostand,
wahlkreis, unterwegs, aktion _(thanks, thank, super, good, atmosphere, fun,
info booth, constituency, on the road, action)_
Coalition Formation: koalition, grosse, mehrheit, waehler, waehlen, grossen,
muessen, bundestag, demokratie, opposition _(coalition, grand, majority,
voter, vote, grand, must, bundestag, democracy, opposition)_
Demonstrations: nazis, demo, baden, wuerttemberg, angst, innen, wasser,
freiheit, platz, protest _(nazis, demo, baden, wuerttemberg, fear, inside,
water, freedom, square, protest)_
Misconduct: paedophilie, zurueck, treten, debatte, verfassungsschutz, tritt,
unfassbar, arbeit, ruecktritt, aufloesen _(pedophilia, back, step, debate,
internal intelligence service, step, inconceivable, work, resignation, close)_
NSA Surveillance: snowden, edward, moskau, treffen, trifft, brief, nsa,
respekt, germany, russland _(snowden, edward, moscow, meeting, meet, letter,
nsa, respect, germany, russia)_
Parliamentary Procedures: bundestag, nsu, sitzung, fraktion, bundestages,
fraktionssitzung, landesgruppe, bundestagsfraktion, rheinland, rede
_(bundestag, nsu, session, faction, bundestag, caucus, regional group,
bundestag faction, rhineland, speech)_
Political Debates: tvduell, waehlen, danke, bayern, dreikampf, zeit, richtig,
beide, duell, kanzlerin _(tvduell, vote, thank, bavaria, triathlon, time,
right, both, duel, chancellor)_
Polity II: leben, muessen, land, geld, buerger, wissen, richtig, volk, kinder,
freiheit _(live, must, country, money, citizens, know, right, people,
children, freedom)_
Post Election: glueckwunsch, herzlichen, bundestag, ergebnis, erfolg,
geburtstag, dank, danke, nsa, gewaehlt _(congratulation, cordial, bundestag,
result, success, birthday, thank, thanks, nsa, elected)_

Table LABEL:tab:topics lists the ten most probable words per topic in German
and English. The topics are very well interpretable. Overall, the model
successfully assigned the social media documents to the known classes when
appropriate. With the exception of _NSA Surveillance_ (which is assigned to
_Law & Order_ in the survey), no new topic mirroring an existing one was
created. An inspection of the documents shows that the new topic focuses on
Edward Snowden and Russia, while the topic from the survey concentrates on the
implications of NSA spying for citizens. One new topic, _Polity II_ , revolves
around the role of citizens in, and criticism of, Germany’s political
structure. The topic is distinct from _Polity I_ in the survey, which is more
about the role of politicians in the polity. Besides these two topics, all
other new topics are related to political processes. Campaign events leave
particularly strong marks in the topic _Political Debates_ , which is the
largest new topic and also the most ambiguous one, as it contains a great
variety of exchanges between politicians and audiences. Especially the
televised debate is prominently featured, which concurs with Kreiss (2016) who
showed that Twitter is used by campaigns for the engagement with audiences and
journalists during high-attention events.

Table 2: Topic salience per corpus (in percent) |  | Politicians | Audience
---|---|---|---
| Survey | Facebook | Twitter | Facebook | Twitter
_Known topics from the survey_ |  |  |  |  |
Labor Market | 19.1 | 4.9 | 6.3 | 8.3 | 6.3
General Social Policy | 12.9 | 1.1 | 1.5 | 1.4 | 1.4
Currency & Euro | 12.5 | 1.6 | 2.6 | 1.9 | 2.2
Education | 7.2 | 1.1 | 1.4 | 0.5 | 1.1
Economy | 7.0 | 0.5 | 0.6 | 0.5 | 0.6
Infrastructure | 6.8 | 3.0 | 5.7 | 1.5 | 5.1
Health Care & Pensions | 5.7 | 0.9 | 1.0 | 0.5 | 0.7
Migration & Integration | 4.0 | 0.2 | 0.2 | 0.1 | 0.3
Polity I | 4.0 | 0.4 | 0.3 | 0.3 | 0.2
Family Policy | 3.3 | 2.4 | 2.4 | 2.3 | 3.0
Law & Order | 3.3 | 3.9 | 7.5 | 3.9 | 9.4
Foreign Policy (Defense) | 2.9 | 2.4 | 3.4 | 2.3 | 3.2
Budget & Debt | 2.7 | 0.2 | 0.2 | 0.1 | 0.1
Taxes | 2.6 | 0.2 | 0.3 | 0.1 | 0.2
Foreign Policy (Europe) | 2.2 | 0.1 | 0.1 | 0.2 | 0.1
General Fiscal Policy | 1.7 | 0.1 | 0.1 | 0.0 | 0.1
Environment | 1.5 | 0.1 | 0.1 | 0.0 | 0.1
Politics | 0.6 | 3.8 | 0.8 | 1.3 | 0.7
_New topics found on social media_ |  |  |  |  |
Campaigning (Local) |  | 21.2 | 13.7 | 5.6 | 7.4
Campaigning (Events) |  | 21.1 | 12.4 | 1.9 | 4.6
Political Debates |  | 8.5 | 13.6 | 21.1 | 18.8
Polity II |  | 5.7 | 7.2 | 22.0 | 13.2
Coalition Formation |  | 5.6 | 6.7 | 12.0 | 8.1
Post Election |  | 3.8 | 3.8 | 6.9 | 4.8
Parliamentary Procedures |  | 3.3 | 3.2 | 0.5 | 1.4
Demonstrations |  | 2.3 | 2.8 | 0.5 | 2.0
NSA Surveillance |  | 0.4 | 1.0 | 0.4 | 3.1
Misconduct |  | 0.3 | 0.5 | 0.3 | 1.4
Note: Ranked by first given column. |  |  |  |  |

Table 2 shows topic saliences in the five corpora (in percent). The first
clear finding is that core policy areas like _Labor Market_ and _General
Social Policy_ have a much higher salience in the survey than on social media.
This resembles the results of Althaus Tewksbury (2002) who showed that readers
of the paper version of the New York Times were more likely to expose
themselves to “public affairs coverage” than the users of its online version.
Among policies, _Infrastructure_ , _Family Policy_ and _Foreign Policy
(Defense)_ are all discussed frequently by politicians and audiences on social
media. Aspects regarding NSA surveillance in the topic _Law & Order_ are
clearly overrepresented in messages by the audience and politicians on Twitter
(see also Jungherr ., 2016). The latter finding demonstrates that the text
analysis model performs well in allocating social media messages to known
topics from the survey. Besides the discussed policy areas, politicians and
audiences alike mostly address different topics than the ones salient in the
survey, which lends support to H1.

It also becomes apparent that politicians use Facebook and Twitter in
different ways. Their summed share of messages related to both _Campaigning_
topics is 42.3% on Facebook compared to 26,1% on Twitter. In contrast,
_Political Debates_ take up a higher share of politicians’ tweets, and the
latter medium is also used more extensively to discuss various policies like
_Infrastructure_ and _Law & Order_. This indicates that politicians tailor
their messages to different media logics and audiences. In line with our
medium specific hypotheses, candidates use Twitter for the commentary of
policies and unfolding public events (H3a), while trying to mobilize Facebook
users to attend campaign events (H3b).

Political audiences use social media overwhelmingly for _Political Debates_ ,
to scrutinize the relationship between state and citizens (_Polity II_) and
comment on _Coalition Formation_. While politicians and audiences are more in
sync on Twitter in that regard, there is a considerable disconnect between
politicians and their audiences on Facebook. Although politicians devoted
42.3% of their messages to campaigning, their audiences mostly talked about
other topics.

Table 3: Rank correlations of topic salience in all corpora | Survey | Facebook | Twitter | Facebook
---|---|---|---|---
|  | politicians | politicians | audience
Facebook politicians | 0.43 |  |  |
Twitter politicians | 0.59∗ | 0.95∗∗∗ |  |
Facebook audience | 0.53∗ | 0.91∗∗∗ | 0.90∗∗∗ |
Twitter audience | 0.58∗ | 0.89∗∗∗ | 0.95∗∗∗ | 0.92∗∗∗
Note: Spearman’s rho. For pairs including the survey, N=$18$. For social media
pairs, N=$28$. ${}^{***}p<0.001$, ${}^{**}p<0.01$, ${}^{*}p<0.05$

A systematic way to analyze topic salience at the document level is to
correlate the ranks of topics in each corpus using Spearman’s rho. Several
findings can be inferred from Table 3. First, comparing the topic ranks in the
survey with social media reveals varying results. Topic saliences in messages
by politicians on Twitter ($p=0.011$), the Facebook audience ($p=0.024$) and
the Twitter audience ($p=0.012$) are all rather similar to topic salience
among a mass audience. Second, topic ranks of all social media corpora are
nevertheless correlated more strongly with other social media corpora than
with the survey ($p<0.001$).66endnote: 6The correlations between all social
media corpora are very similar and highly significant ($p<0.001$) when only
using the 18 known topics from the survey as input. Third, correlations
between the topic ranks in the survey and politicians’ Facebook posts are
weaker than in case of the other social media corpora ($p=0.075$). This lends
support to H2 which postulates that candidates address topics relevant to a
mass audience on Twitter while discussing such topics more sparsely on
Facebook.

We learn from these distributions that the messages of social media users are
shaped by considerable mediation effects, which provides further evidence for
H1. On social media, a specific subset of politically engaged citizens
discusses specific topics via specific sociotechnical transmission mechanisms.
Politicians seem to adjust to these mediated environments and thus have
remarkably similar topic ranks like the personal networks they are most
directly exposed to. They adopt the public communication practices of the
Twittersphere (Wu ., 2011) and use the medium primarily for political
commentary, while trying to mobilize their interested followers on Facebook
for campaign purposes. At the same time, correlations between the different
content layers on social media – except for the Facebook politicians corpus –
and the survey are still strong. Therefore, the stark differences in the sizes
of topics per corpus revealed in Table 2 are reduced considerably when
comparing the ranks of topics. When politicians and audiences talk about
policies on social media, they tend to prioritize similar topics like
respondents in a representative survey. This indicates that the public agenda
is still rather cohesive during election campaigns – independent of the
medium.

### Analysis at the word level

The analyses at the document level revealed the salience of political topics
in different media and content layers. But with the textual data at hand, we
can move our analysis to the word level, which serves two purposes. First, it
adds methodological robustness to the previous results. Given different types
of media, it is to be expected that the language encountered in corpora is
distinct, since specific conventions and space restrictions apply to interview
situations, Facebook messages or tweets. The following analysis will rule out
that differences in salience are mere artifacts of platform specific talk.
Second, we can also investigate how different topics are perceived and talked
about by a mass audience, politicians and their social media audiences. By
that, we move beyond the salience of topics toward an identification of
similar perspectives regarding political topics in different corpora.

Since the underlying vocabulary in our model is the same across all five
corpora, we can compare the topic-word distributions across media and content
layers. For this, we calculate cosine similarities (scale 0 to 1) between all
corpus pairs in each topic. Figure 2 displays the results, organized in
decreasing order by the average cosine similarity per topic. The darker a
cell, the more similarly a corpus pair discusses a topic. Cells of social
media specific topics remain blank for the survey since the topics are not
featured in this corpus. It becomes clear from the visualization that
similarities between corpora vary considerably depending on the topic.

Figure 2: Cosine similarities between corpora by topic.

In our final analysis, we take the values of the 240 cells in Figure 2 as the
dependent variable in Ordinary Least Squares regressions.77endnote: 7Since
most models were heteroscedastic, we use robust standard errors. This allows
us to assess if our hypotheses regarding similarities between different media
and content layers hold at the level of words used in political communication.
As independent variables, we construct a dummy indicating that the survey is
part of a given corpus pair and, similarly, if the politicians’ Facebook or
Twitter corpus is featured in a corpus pair. We also take control variables
into account that could influence the relationships between cosine
similarities and the independent variables. First, we include a logged
variable counting the combined number of tokens (the aggregate word count) in
each corpus pair per topic. By that we take the size of corpus pairs into
account which could systematically affect cosine similarities. We added
dummies indicating that a corpus pair is from the same social medium, i.e.,
Facebook or Twitter, or that the group (politicians, audiences) in a corpus
pair is the same. Moreover, we use the information from GLES on topic types,
i.e., whether a topic belongs to the categories policy, politics or policy and
classified the ten new social media topics analogously. Based on that, we
construct a dummy signaling that a topic is related to politics aspects, which
we assume are discussed more heterogeneously than the polity or
policies.88endnote: 8This is not a hard distinction but rather an exploratory
application of the GLES labels. In fact, it is difficult to distinguish policy
aspects from politics aspects related to an issue. Finally the 10 new topics
identified on social media are marked by a dummy variable to account for
differences in topic origin (_New topics_).

Table 4: Models of cosine similarities between corpus pairs | (1) | (2) | (3) | (4) | (5)
---|---|---|---|---|---
Survey | $-0.12^{***}$ | $-0.12^{***}$ | $-0.10^{**}$ | $-0.11^{***}$ | $-0.09^{**}$
| $(0.03)$ | $(0.03)$ | $(0.03)$ | $(0.03)$ | $(0.03)$
Facebook politicians |  | $0.00$ | $0.02$ |  |
|  | $(0.02)$ | $(0.02)$ |  |
Twitter politicians |  |  | $0.05^{*}$ |  | $0.06^{**}$
|  |  | $(0.02)$ |  | $(0.02)$
Twitter audience |  |  |  | $0.03$ | $0.05^{*}$
|  |  |  | $(0.02)$ | $(0.02)$
Number of tokens (logged) | $0.14^{***}$ | $0.14^{***}$ | $0.14^{***}$ | $0.14^{***}$ | $0.14^{***}$
| $(0.01)$ | $(0.01)$ | $(0.01)$ | $(0.01)$ | $(0.01)$
Same medium | $0.08^{**}$ | $0.08^{**}$ | $0.08^{**}$ | $0.08^{**}$ | $0.08^{**}$
| $(0.03)$ | $(0.03)$ | $(0.03)$ | $(0.03)$ | $(0.03)$
Same actor | $0.05$ | $0.05$ | $0.05$ | $0.05$ | $0.05$
| $(0.03)$ | $(0.03)$ | $(0.03)$ | $(0.03)$ | $(0.03)$
Topic type (politics=1) | $-0.10^{*}$ | $-0.10^{*}$ | $-0.10^{*}$ | $-0.10^{*}$ | $-0.10^{*}$
| $(0.05)$ | $(0.05)$ | $(0.05)$ | $(0.05)$ | $(0.04)$
New topics | $-0.02$ | $-0.02$ | $-0.02$ | $-0.02$ | $-0.01$
| $(0.05)$ | $(0.05)$ | $(0.04)$ | $(0.05)$ | $(0.04)$
Constant | $-0.59^{***}$ | $-0.59^{***}$ | $-0.61^{***}$ | $-0.60^{***}$ | $-0.62^{***}$
| $(0.07)$ | $(0.07)$ | $(0.07)$ | $(0.07)$ | $(0.07)$
R2 | 0.63 | 0.63 | 0.63 | 0.63 | 0.64
Adj. R2 | 0.62 | 0.61 | 0.62 | 0.62 | 0.63
N | 240 | 240 | 240 | 240 | 240
Note: OLS with robust standard errors in parentheses. ${}^{***}p<0.001$, ${}^{**}p<0.01$, ${}^{*}p<0.05$ |  |

Table 4, Model 1 shows that the corpus pairs including the survey have an
approximately 12% lower cosine similarity, other things being equal. This
demonstrates that in addition to the differences in topic salience identified
before (H1), the description of political problems in the survey is also
markedly different from the average topically related social media message. In
Model 2 and Model 3, we see that contrary to their posts on Facebook, the
language in tweets by politicians is significantly more similar to other
corpora than the average corpus pair. This means that on Twitter, candidates
emphasize aspects of topics important to survey respondents, but also use
language similar to other content layers on social media. Such a hybrid
communication strategy by politicians can be interpreted as a synergy of mass
communication strategies and messages targeted at individual follower networks
on social media, much in line with the notion “masspersonal” ascribed to
Twitter (Wu ., 2011).

Several more factors could influence these results. First, throughout Table 4
the number of underlying tokens is highly correlated with the cosine
similarity between the corpus pairs. This is to be expected by the law of
large numbers as the empirical word distributions in both corpora become more
stable with an increasing number of tokens and cosine similarities increase.
Controlling for the size effect ex post in a multivariate regression model
allows for a systematic comparison of similarities between corpus pairs.
Second, politicians’ tweets might reflect a typical communication style on
Twitter and thus not be indicative of strategic considerations. To test this,
we include a Twitter audience dummy in Model 4 without finding a significant
effect. When including the Twitter audience dummy in combination with the
Twitter politicians dummy in Model 5, it becomes significant, but the Twitter
politicians variable still has superior explanatory power.99endnote: 9We do
not include all corpus dummies at once because as part of corpus pairs, the
dummies are not mutually exclusive. Therefore, we cannot exclude one dummy as
the reference category and interpret results accordingly, as it is usually
done with multiple-category variables. Third, there is nonetheless evidence
for platform effects, as _Same medium_ is a significantly positive predictor
($p\leq 0.009$ in all models). This demonstrates that politicians and
audiences not only emphasize similar topics (H1) but also address similar
aspects when talking about a topic on the same social media platform. Fourth,
the results also hold when controlling for the insignificant variable _Same
actor_ , which means that audiences and politicians adopt distinct
communicative practices when using different media. Fifth, the topics
primarily related to politics are more semantically diverse than the topic
types polity and policy. Finally, similarities do not differ systematically
depending on whether a topic is known from the survey or newly created from
social media (_New topics_). This is of particular methodological importance,
as it demonstrates that semantic differences and the measurement of topic
salience at the document level are not just artifacts of stark differences in
the two communication situations interview and social media message. Finding a
systematic effect of the two data generation modes would have questioned the
validity of applying a model based on survey responses to social media
data.1010endnote: 10The main results are robust when rerunning the regression
models using only known topics from GLES (with N=180 cells in Figure 2) and
also when solely taking the new social media topics into account (N=60 cells).
The exception is the model with only known topics, in which the Twitter
politicians and audience dummies lose their significance. Since some of the
dummy variables overlap considerably, we also ran broader and smaller models
with varying constellations of included variables, which confirm the main
findings.

In sum, Table 4 reveals similarities in political communication between
corpora that resemble the ranks of topic salience at the document level.
Still, problem descriptions in the survey and social media are more distinct
than to be expected from Table 3. This shows that overlaps in topic salience
can still mask _how_ topics are talked about and perceived by different
audiences.1111endnote: 11Although agenda-setting is outside the scope of our
study, this distinction resembles the conceptualization of first- and second-
level effects found in the related literature (McCombs ., 1997). We also learn
that the distinct uses of Facebook and Twitter by politicians are not just
byproducts of media specific language, but are rather driven by the strategic
considerations we discuss in our theory. Moreover, the analysis at the word
level makes the topic generation process more transparent and increases our
understanding of mediation processes in political communication.

## Conclusion

This study provides insights into how political communication is shaped by
social media. We compared the topics of most importance to a mass audience in
a representative survey to the topics discussed on Facebook and Twitter. Based
on a text analysis model developed specifically for this purpose, we show that
politicians and their audiences discuss different topics on social media than
those salient among a mass audience. Moreover, politicians use Facebook and
Twitter for different purposes which we relate to the distinct target groups
candidates encounter. Taken together, our findings suggest that campaign
strategies and political communication in general are mediated by varying
sociotechnical affordances of social media platforms.

These results challenge previous findings in political communication which
indicated that politicians use the web rather conservatively and in a non-
interactive manner (Druckman ., 2010; Stromer-Galley, 2000; Lilleker ., 2011;
Gibson ., 2014; Larsson, 2015). However, our study does not necessarily
contradict previous research, as each Internet application has specific
affordances and politicians demonstrate a strategic awareness of various
communication arenas. They complement the “masspersonal” communication (Wu .,
2011) in the quasi-public sphere of Twitter with the more direct communication
practices on Facebook for organizational and mobilization purposes. Our cross-
media study also shows that relevant differences in political communication
exist between social media platforms. This underscores the need to argue with
the utmost caution when trying to infer findings from one platform to “social
media” as a whole, as it has often been done.

Several findings indicate that the high-choice media environments of social
media contribute to a fragmentation of the mass audience (Althaus Tewksbury,
2002; Prior, 2007; McQuail, 2010, p. 140). Politicians and their audiences
talk about policies sparsely, but rather discuss campaign-related events and
topics specific to social media. Their salient topics resemble each other
remarkably, compared to the priorities of survey respondents. Furthermore, the
language used indicates that other aspects of similar topics are addressed on
social media than in survey responses. Those differences notwithstanding, when
candidates and audiences actually discuss policies on Facebook and Twitter,
they prioritize those similarly like respondents in a representative survey.
In this regard, the public agenda is still rather integrated. This points
towards persistent – although probably diffuse and mediated – agenda-setting
effects between mass media and social media (Neuman ., 2014) as well as within
social media. A fruitful research avenue would be to focus on the interactions
of politicians and audiences with accounts of journalists and legacy media
which bridge the gap between the general public and more particular sets of
audiences on social media.

We also want to discuss the limitations of this study. The article focused on
an election campaign with several high-attention events that were most
commented-on on social media. A follow-up study during non-election periods
could possibly reveal more issue-related communication in line with the
preferences of a mass audience. Demographic information on the follower graphs
of politicians on Facebook and Twitter would certainly enhance our findings,
however, such data is not readily available to researchers. While our single-
membership model is well suited for the short nature and narrow focus of
social media messages, its downside is that the more multidimensional and
textually rich messages necessarily also have to be allocated to just one
topic. Moreover, due to restrictions in survey data containing an MIP and the
considerable efforts required to mine social media data of all election
candidates, we only investigated one campaign in one country. On the one hand,
the mediation effects we found should travel well, as German social media
campaigning was still in its infancy in 2013 and severe regulatory
restrictions on micro-targeting apply (Stier, 2015). More sophisticated
campaigns elsewhere might be better at addressing the particular needs of
digital media. On the other hand, the rather ad hoc campaign style and
personal use of accounts by several candidates in our case might be more
suited for the peculiarities of social media than, for instance, the highly
professionalized U.S. campaigns. Therefore, it remains to be seen to which
extent the findings are applicable to online campaigning in other countries
and the (near) future.

The paper makes a methodological contribution to the field of political text
analysis (Grimmer Stewart, 2013) by proposing a novel methodology to apply
labeled text to a test corpus while also allowing for the introduction of
additional topic categories. The method is applicable in a vast range of
research contexts in political communication in which a test dataset deviates
from a predefined coding scheme. Substantively, the paper shows that mediation
effects induced by social media platforms and their sociotechnical
environments are strongly felt in political communication. This means that
social media is not an ideal data source for citizens seeking clearly
structured information on policies or researchers using textual information to
locate parties in an ideological space.

## Acknowledgements

We thank three reviewers, the editors and participants at the ECPR General
Conference 2016 for helpful comments.

###### Notes

  1. 1 The wording is: “In your opinion what is the most important political problem facing Germany at the moment?” (Rattinger ., 2014).
  2. 2 Before the election, 7,249 participants (92%) stated a MIP and 6,673 (85%) stated a second MIP. After the election, 5,016 respondents (64%) stated a MIP and 4,666 (59%) stated a second MIP.
  3. 3 4,220 (54%) stated four problems, 543 (7%) stated three, 2,364 (30%) stated two and 367 (5%) stated one problem. 388 (5%) did not know a problem or did not respond.
  4. 4 The data mining of Facebook posts ended three days earlier, on 31 October 2013.
  5. 5 We took audience tweets in which only one politician was mentioned for the sampling.
  6. 6 The correlations between all social media corpora are very similar and highly significant ($p<0.001$) when only using the 18 known topics from the survey as input.
  7. 7 Since most models were heteroscedastic, we use robust standard errors.
  8. 8 This is not a hard distinction but rather an exploratory application of the GLES labels. In fact, it is difficult to distinguish policy aspects from politics aspects related to an issue.
  9. 9 We do not include all corpus dummies at once because as part of corpus pairs, the dummies are not mutually exclusive. Therefore, we cannot exclude one dummy as the reference category and interpret results accordingly, as it is usually done with multiple-category variables.
  10. 10 The main results are robust when rerunning the regression models using only known topics from GLES (with N=180 cells in Figure 2) and also when solely taking the new social media topics into account (N=60 cells). The exception is the model with only known topics, in which the Twitter politicians and audience dummies lose their significance. Since some of the dummy variables overlap considerably, we also ran broader and smaller models with varying constellations of included variables, which confirm the main findings.
  11. 11 Although agenda-setting is outside the scope of our study, this distinction resembles the conceptualization of first- and second-level effects found in the related literature (McCombs ., 1997).

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##  Appendix A Appendix A: Description of the model

Following the common notation for probabilistic language models, we treat a
GLES response $d$ as a tuple of a topic indicator variable
$z^{\mathcal{G}}_{d}$ and a vector $\mathbf{w}^{\mathcal{G}}_{d}$ of observed
words. The indicator $z^{\mathcal{G}}_{d}\in[1,...,\hat{K}]$ is an assignment
to one of $\hat{K}$ initial GLES topics, and each of the $n^{\mathcal{G}}_{d}$
observed words $w^{\mathcal{G}}_{di}$ in $\mathbf{w}^{\mathcal{G}}_{d}$ is
chosen from a vocabulary of $V$ terms, leading to the collection of GLES
responses
$\mathcal{D}^{\mathcal{G}}=\\{(\mathbf{w}^{\mathcal{G}}_{d},z^{\mathcal{G}}_{d}),...,(\mathbf{w}^{\mathcal{G}}_{D^{\mathcal{G}}},z^{\mathcal{G}}_{D^{\mathcal{G}}})\\}$.
Analogously, we treat a social media message
$(\mathbf{w}^{\mathcal{M}}_{d},z^{\mathcal{M}}_{d})$ as a tuple of words and a
topic assignment. While the $n^{\mathcal{M}}_{d}$ words
$\mathbf{w}^{\mathcal{M}}_{d}$ are chosen from the same vocabulary as in the
case of GLES responses, the topic assignments $\mathbf{z}^{\mathcal{M}}$ are
unobserved and not restricted to the initial $\hat{K}$ topics. The collection
of social media messages is
$\mathcal{D}^{\mathcal{M}}=\\{(\mathbf{w}^{\mathcal{M}}_{d},z^{\mathcal{M}}_{d}),...,(\mathbf{w}^{\mathcal{M}}_{D^{\mathcal{M}}},z^{\mathcal{M}}_{D^{\mathcal{M}}})\\}$.
We refer to the collection of all documents (GLES responses and social media
messages) by
$\mathcal{D}=\mathcal{D}^{\mathcal{G}}\cup\mathcal{D}^{\mathcal{M}}$. The
generative storyline for the documents in our model, i.e. the mechanism
assumed to create responses and messages, can be described by the following
steps.

  1. 1.

For the unrestricted global topic popularity a distribution
$\boldsymbol{\theta}$ is drawn from an infinite dimensional Dirichlet
distribution (i.e., $K\to\infty$) with parameter $\alpha$:

$\displaystyle\boldsymbol{\theta}\sim Dir(\tfrac{\alpha}{K})\mbox{ .}$

  2. 2.

For each topic $k=[1,...,\infty[$, a distribution $\boldsymbol{\phi}_{k}$ over
the vocabulary is drawn from a V-dimensional Dirichlet distribution
parametrized by $\beta$:

$\displaystyle\boldsymbol{\phi_{k}}\sim Dir(\beta)\mbox{ .}$

  3. 3.

The GLES responses $\mathcal{D}^{\mathcal{G}}$ and social media messages
$\mathcal{D}^{\mathcal{M}}$ are then generated by the same mechanism of first
drawing a topic index $z_{d}$ from the topic popularity $\boldsymbol{\theta}$
and subsequently drawing multinomially distributed words $\mathbf{w}_{d}$ from
a topic indexed by $z_{d}$:

$\displaystyle\mathbf{w}_{d}\sim Mult(\boldsymbol{\phi}_{z_{d}})\mbox{ ,}$
$\displaystyle z_{d}\sim Cat(\boldsymbol{\theta})\mbox{,}$

where we omitted the indexes $\mathcal{G}$ and $\mathcal{M}$ for readability,
leading to the joint probability for the model

$\displaystyle
p(\mathcal{D},\mathbf{z},\boldsymbol{\theta},\boldsymbol{\phi})=$
$\displaystyle Dir(\boldsymbol{\theta}\mid\tfrac{\alpha}{K})$
$\displaystyle\prod_{k=1}^{\infty}\,Dir(\boldsymbol{\phi}_{k}\mid\beta)$
$\displaystyle\prod_{d=1}^{|\mathcal{D}^{\mathcal{G}}|}\,Mult(\mathbf{w}^{\mathcal{G}}_{d}\mid\boldsymbol{\phi}_{z^{\mathcal{G}}_{d}})\,Cat(z^{\mathcal{G}}_{d}\mid\boldsymbol{\theta})$
$\displaystyle\prod_{d=1}^{|\mathcal{D}^{\mathcal{M}}|}\,Mult(\mathbf{w}^{\mathcal{M}}_{d}\mid\boldsymbol{\phi}_{z^{\mathcal{M}}_{d}})\,Cat(z^{\mathcal{M}}_{d}\mid\boldsymbol{\theta})\mbox{
.}$

Figure A1 gives a graphical representation of our model.

$\boldsymbol{\theta}$$\alpha$$z^{\mathcal{M}}_{d}$$\mathbf{w}^{\mathcal{M}}_{d}$$\mathbf{w}^{\mathcal{G}}_{d}$$z^{\mathcal{G}}_{d}$$\boldsymbol{\phi}_{k}$$\beta$$\forall\;d\in[1,D^{\mathcal{G}}]$$\forall\;d\in[1,D^{\mathcal{M}}]$$\forall\;k\in[1,\infty[$
topic-word distributions words in social media messages unknown topic
assignment global topic popularity observed topic assignment words in GLES
responses

Figure A1: Model in Plate Notation: Random variables are represented by nodes.
Blank nodes are used for the (unknown) hidden variables $\boldsymbol{\theta}$,
$\boldsymbol{\phi}$ and $\mathbf{z}^{\mathcal{M}}$. Shaded nodes denote the
observed words $\mathbf{w}^{\mathcal{M}}$ and $\mathbf{w}^{\mathcal{G}}$ as
well as the observed labels $\mathbf{z}^{\mathcal{G}}$, bare symbols indicate
the fixed priors $\alpha$ and $\beta$. Directed edges between the nodes then
define conditional probabilities, where the child node is conditioned on its
parents. The plate surrounding $\boldsymbol{\phi}_{k}$ indicates the
replication of the node for each mixture component and the plates surrounding
$\mathbf{w}^{\mathcal{G}}_{d}$ and $\mathbf{w}^{\mathcal{M}}_{d}$ indicate the
replication of the nodes over the $\mathcal{D}^{\mathcal{G}}$ labeled and
$\mathcal{D}^{\mathcal{M}}$ unlabeled documents, respectively.

##  Appendix B Appendix B: Description of the inference

For inference, we resort to collapsed Gibbs sampling of the latent indicator
variables $\mathbf{z}^{\mathcal{M}}$, with all other variables integrated out.
The full conditional posterior probability for a topic $k$ is then given by

$\displaystyle p(z^{\mathcal{M}}_{d}=k)$
$\displaystyle\propto\begin{cases}\dfrac{{}^{\neg d}n_{k}}{{}^{\neg
d}n_{.}+\alpha}\;\;p(\mathbf{w}^{\mathcal{M}}_{d}\mid{}^{\neg
d}\mathbf{w}^{\mathcal{M}}_{z=k},\mathbf{w}^{\mathcal{G}}_{z=k},\beta)&\text{if
}k\leq K\\\\[10.00002pt] \dfrac{\alpha}{{}^{\neg
d}n_{.}+\alpha}\;\;p(\mathbf{w}^{\mathcal{M}}_{d}\mid\beta)&\text{if
}k=K+1\mbox{ ,}\end{cases}$

where $n_{k}=\sum_{d=1}^{D}\mathbbm{1}[z_{d}=k]$ is the number of documents
and $\mathbf{w}_{z=k}$ are the words assigned to the $k^{th}$ topic. The
superscript $\neg d$ indicates that the current document $d$ is excluded from
consideration. We further use a approximation for the likelihood of message
$d$ under topic $k$

$\displaystyle p(\mathbf{w}^{\mathcal{M}}_{d}\mid{}^{\neg
d}\mathbf{w}^{\mathcal{M}}_{z=k},\mathbf{w}^{\mathcal{G}}_{z=k},\beta)$
$\displaystyle=\int_{\phi_{k}}Mult(\mathbf{w}^{\mathcal{M}}_{d}\mid\boldsymbol{\phi}_{k})\,Dir(\boldsymbol{\phi}_{k}\mid\hat{\beta}_{k}^{\neg
d})\;d\ \boldsymbol{\phi}_{k}$
$\displaystyle\approx\prod_{i=1}^{n_{d}^{\mathcal{M}}}\dfrac{{}^{\neg
d}n_{kw_{di}}^{\mathcal{M}}+n_{kw_{di}}^{\mathcal{G}}+\beta}{{}^{\neg
d}n_{k.}^{\mathcal{M}}+n_{k.}^{\mathcal{G}}+V\beta}\mbox{ ,}$

with $n_{kw}$ being the number of times term $w$ is used in topic $k$.
Likewise, the likelihood of $\mathbf{w}^{\mathcal{M}}_{d}$ being generated
from a yet unseen topic is

$\displaystyle p(\mathbf{w}^{\mathcal{M}}_{d}\mid\beta)$
$\displaystyle=\int_{\phi}Mult(\mathbf{w}^{\mathcal{M}}_{d}\mid\boldsymbol{\phi})\,Dir(\boldsymbol{\phi}\mid\beta)\;d\phi$
$\displaystyle\approx\dfrac{1}{V^{n_{d}^{\mathcal{M}}}}\mbox{ .}$

Given the priors $\alpha$ and $\beta$, Gibbs sampling in this model reduces to
iteratively sampling the indicator variables $\mathbf{z}^{\mathcal{M}}$ from
their full conditional posterior probability. Once a sufficient number of
iterations is completed, the distribution of the indicator variables and as
such the number of topics $K$ with associated observations is independent from
its initialization and the sampler has converged. For a more in-depth
discussion of Gibbs sampling for Dirichlet process mixture models the
interested reader is referred to Neal (2000).

##  Appendix C Appendix C: Survey dataset

Topics useful for the training of the model should be sufficiently
discriminative and relatively equal in size. The Rolling-Cross-Section survey
of the German Longitudinal Election Study (GLES) allows for answers to be
coded as politics, polity, or one of 13 policy areas. From the latter, three
were removed (206 answers on “other” problems, 102 on East Germany, and 1 on
cultural leisure policy). The sizes of the resulting areas ranged from 188
(“politics”) to 6,010 (“social policy”) observations. For our purposes, some
areas had to be split by testing different subtopic combinations and
qualitatively assessing topic overlaps, while others could be used as they
are.

In essence, six areas were kept as they are. “Social policy” was split into
_General Social Policy_ , _Family Policy_ , _Health Care & Pensions_ and
_Migration & Integration_. “Fiscal policy” was split into _General Fiscal
Policy_ , _Currency & Euro_, _Taxes_ and _Budget & Debt_. GLES topics on
foreign policy and defense policy were merged into a training topic on
_Foreign Policy (Defense)_ , but the general subtopic on Europe was extracted
to stand alone. As a result, we obtained 18 discriminative topics (totalling
23,295 responses in one politics class, one polity class and 16 policy
classes). The coding is reproducible from Table A1.

Table A1: Construction of training topics.

Label | Observations | GLES Codes
---|---|---
Politics | 188 | 1XXX
Polity I | 1,063 | 2XXX
Budget & Debt | 629 | 431X
Currency & Euro | 2,830 | 433X
Economy | 1,618 | 39XX, 40XX
Education | 1,624 | 41XX
Environment | 352 | 36XX
Family Policy | 776 | 371X
Foreign Policy (Defense) | 701 | 310X, 312X, 316X, 317X, 318X, 329X, 330X, 331X, 332X, 333X, 339X
Foreign Policy (Europe) | 561 | 311X
General Fiscal Policy | 389 | 430X, 439X
General Social Policy | 2,950 | 370X, 372X, 373X, 378X, 379X
Health Care & Pensions | 1,333 | 374X, 376X, 377X
Infrastructure | 1,580 | 35XX
Labor Market | 4,358 | 38XX
Law & Order | 793 | 34XX
Migration & Integration | 951 | 375X
Taxes | 599 | 432X

  * •

Note: Topic codes from GLES (2013).