3 Data and Workflow
The empirical case study in this article is based on New Year’s speeches of the three
German-speaking countries, Germany, Austria and Switzerland
[2] covering
the years from 2000 to 2021. These three countries were selected, first, based on the
shared language providing us the possibility to compare the use of language of high
politics across these countries. Second, all three countries belong to the same
cultural-political space, Central Europe, with a strong, continental orientation and a
centuries-long experience and tradition of German cultural, economic, and political
influence, but also dominance [
Jordan 2005]. Besides the two more or less
uniting criteria, the third selection criterion was a dividing factor: Germany and
Austria are members in the European Union (EU), whereas Switzerland is only indirectly
involved in European integration through its membership in the European Free Trade
Association (EFTA) and a set of bilateral agreements with the EU. Switzerland is also
member of the Schengen area allowing any person, irrespective of nationality, to cross
the internal borders without being subjected to border checks. Currently, most of the EU
countries plus the non-EU countries Iceland, Norway, Switzerland and Liechtenstein have
joined the Schengen area. Against this background I expect these three countries to form
an “imagined community” (Benedict Anderson) and to share certain values, interests and
world views reflected by the New Year’s addresses. Further, I expect German and Austrian
New Year’s speeches to share discursive patterns related to EU affairs, especially to
the euro crisis (since 2008), the conflict in Ukraine (since 2014), and the refugee
crisis (since 2015).
[3] I also could
explore the impact of the global Covid-19 pandemic disease on both the content and
vocabulary across the three countries yet this impact is visible only in the speeches
for the year 2021.
New Year’s speeches held by leading politicians–mostly prime ministers or
presidents–have a long and firm tradition in Europe and “have become an institution
instead of being a crowing event for conciliatory efforts” [
Portman 2014, 89].
From the perspective of political communication, New Year’s addresses
fulfil a triple function in the intersection of the past, the present and the future.
First, they summarise the past year from the perspective of the political leadership
and, hence, recall, reconstruct and remind the most important events of the year.
Second, New Year’s speeches describe the present and, thus, can be understood and
analysed as reality constructions, as windows to the current state of affairs. And
third, New Year’s speeches serve as road maps to the future, into the new year. In this
sense, a New Year’s speeches summarises the most important future challenges,
expectations and opportunities.
There exist only a small number of dedicated studies on New Year’s speeches [
Heikkinen 2006]
[
Portman 2014] [
Purhonen and Toikka 2016]. I could not identify
any specific reason for this, despite that New Year’s speeches are dominantly given in
non-English speaking countries in Europe. As a text genre, however, New Year’s addresses
are comparable with the Presidential Inaugural speeches of the U.S. and this article
methodologically benefits from the study of [
Light 2014] on the United States’ Inaugural
Addresses. From the perspective of social sciences I am not primarily interested in the
use of language, but issues mentioned and described in the addresses. This viewpoint is
rooted in the assumption that a political phenomenon, topic or event gains in importance
if mentioned in a New Year’s address. Here I also follow the idea that New Year’s
speeches offer insights into shifts in political priorities, interests, and
opinions [
Light 2014]. Against this background I am convinced that a
longitudinal, computational analysis of German, Austrian and Swiss New Year’s addresses
can help us to a better understanding of how the political landscape has changed between
2000 and 2021, but also where the differences and similarities can be found between
these three countries.
The speech corpus covers a period of 22 years and contains 64 speeches. There are two
missing years – 2002 and 2017 – in the Austrian speech collection. In Austria Federal
President delivers the New Year’s speeches and between 2000 and 2004 the speeches were
delivered by Thomas Klestil, between 2005 and 2016 by Heinz Fischer, and since 2018 by
Alexander Van der Bellen. Although speeches from 2005 onwards can be downloaded from the
official website of the Austrian Federal President.
Fortunately, the speeches of 2000-2001 and 2003-2004 could be found in different
Austrian media archives. As regards the year 2002, neither the President’s office nor
the National Archive of Austria could provide any information about where this
particular New Year’s speech could be found, so that I was forced to drop this year from
the corpus. In 2017 Austria had an extraordinary political situation with no Federal
President in office, so that no New Year’s speech was given.
In Germany, the New Year’s speeches are held by Federal Chancellor and full texts are
available on the
official website.
[4]. The first six speeches
(2000-2005) were held by Chancellor Gerhard Schröder, the remaining sixteen from 2006 to
2021 by Chancellor Angela Merkel. In Switzerland, the annual New Year’s speech is
delivered by the President of the Confederation and all speeches since 1980 are
available online on the
official website of the President of the Confederation. As the
term of the Swiss President is just one year all New Year’s speeches are held by a
different person (except Micheline Calmy-Rey, Pascal Couchepin and Moritz Leuenberger,
all of them re-elected and with two speeches).
Despite the varieties in the use of language in the German-speaking Europe, formal
speeches by top-level politicians can be seen as an appropriate source of data, as
policy discourses are comprised of policy addresses and speeches about policy
issues [
Van Dijk 1977].
New Year’s speeches are traditionally broadcasted over TV or radio within a relatively
short time of 10 to 15 minutes. Consequently, these speeches are relative compact in
size. The average speeches length of a German new New Year’s speech was 892 tokens and
standard deviation (sd) was 111. In Austria and Switzerland the average lengths were 719
(sd=58) and 562 (sd=159) respectively (Figure 1). Quite understandably
the amount of unique tokens is somewhat lower, since certain words are used several
times during a speech. For German New Year’s addresses, there were 362 unique tokens in
average (sd=33), for Austrian and Swiss speeches the average number of unique tokens was
317 (sd=27) and 255 (sd=60) respectively. Swiss New Year’s speeches seem to have become
shorter over time, whereas both German and Austrian New Year’s speeches remain relative
stable over time (Figure 1). Generally speaking a country’s size seem to matter here, as German New
Year’s speeches are slightly longer than the Austrian or Swiss addresses. My
explanation for this observed difference stems from the differences in political and
economic importance. Germany’s central role in European politics results in broader
political agenda, so that there are more issues and topics to be addressed to in a New
Year’s address.
As regards the analysis workflow, all collected speeches were saved in separate files in
plain text format. These files were then imported into
RStudio, a graphical end-user environment for the statistical
package R. The research data was created and
analysed in the four steps. First, I used the package ’udpipe’
[5] to
tokenise, part-of-speech tag, and lemmatise the documents. After this step, the data was
structured as a data table consisting of 53893 words and 5277 unique lemmata, enriched
with descriptive metadata about the country and the year, as well sentence indices. In
the third step, I used text mining and explorative data analysis tools from the package
’tidytext’ to carry out different
text analyses and to transform textual data to different sets of text network data. The
applied text mining methods will be described in the next section. For the text network
network creation I used both bi-grams by linking two consecutive words, as well
skipgrams of length two by combining non-adjacent words that were three, four or five
words apart.
[6] Both the
bi-grams and skipgrams were created within the boundaries of the same sentence based on
the sentence index included in the data structure. (2) in the case of concepts used in a
single country, only concepts which occur more than three times are included. Text
networks were visualised and analysed with ’visone’, a fully-fledged, platform
independent software offering powerful layouts for network visualisations and a
comprehensive set of tools for network manipulation,
transformation and analysis.
4 Results
Figure 2 presents the addresses
network for the whole corpus based on the similarity of the New Year’s addresses.
Similarity is here calculated as cosine measure, a term-based comparison between
documents in a document corpus, and has a value range from 0 (no similarity at all) to 1
(full similarity).
[7] I reduced noise in the
document-term matrix by removing both stopwords and all terms except nouns, verbs,
pronouns, and proper nouns. In the network visualisation nodes represent different New
Years speeches and are coloured with country-specific colours so that German addresses
are coloured with yellow, Austrian with red, and Swiss speeches with white. I have also
added labels to help the reader to identify different addresses. Nodes are connected by
edges, of which width is visualised proportional to the cosine measure of the two
speeches connected by that edge. In my data the cosine measures ranged from 0.103
(between the Swiss speeches 2006 and 2020) to 0.641 (between the German speeches 2009
and 2010), but I have removed edges with cosine measure below 0.400 in order to improve
the readability of the network visualisation. Further, I used a force-directed layout
and optimised it to position speeches with higher similarity closer, those with lesser
to each other so that the clusters of more similar speeches are easier to identify.
As regards the similarity network two observations seem appropriate. First, the New
Year’s speeches evidence a stronger similarity within, and weaker similarity across
countries. This is clearly visible in the network structure as well, as speeches from
different countries form clear and distinct cluster in the visualisation (see Figure 2). Overall, the average
similarity for cross-country speeches is 0.318 (min=0.114, max=0.514), for national
speeches the average similarity is 0.384 (min=0.103, max=0.614). If we set the
threshold value for cosine measure to 0.5, only two cross-national combinations, namely
Austria 2018 – Switzerland 2020 (0.505) and Germany 2014 – Switzerland 2018 (0.514),
exceed this limit. Varieties of national political agenda and circumstances seem to
explain these observed differences, thus underlining the primacy of national topical
issues. An interesting result is also that the cosine similarity between German and
Austrian speeches is significantly higher (mean=0.355) than that between
German/Austrian and Swiss speeches (mean=0.277). This difference is at least partly
explained by the EU membership, as certain topical issues both German and Austrian
speeches tackle are connected to EU politics.
And second, similarity seems to be stronger between between chronologically close
speeches and weaker between speeches with greater temporal distance. I tested this
with a simple Pearson’s correlation test and calculated the correlation between the
cosine measure and the temporal distance in years between the New Year’s addresses. The
correlation was -0.085 (p<0.001), thus confirming the hypothesis that the similarity
measure decreases when the temporal distance between compared speeches increases. This
seems logical when reflected against the fact that New Year’s speeches tackle topical
issues at the end of a year. The longer the temporal distance between two addresses, the
more probable there has been significant changes in political circumstances affecting
the vocabulary used in the addresses. Hence, this observation also gives support to the
argument that New Year’s speeches offer an interesting and valuable perspective on a
year’s prevailing political reality.
Results from the document-level TNA suggested the existence of variance in vocabulary
between the countries and over time. In order to better understand this variance I
applied a specific text mining method, called term frequency, inverse document frequency
(tf-idf) analysis, to adjust the frequency of a word for how
rarely it is used in the collection of New Year’s speeches of each country. A word’s
inverse document frequency (idf) was then applied to explore
words that are not used very much during the whole period from 2000 to 2021. I divided
each national collection of New Year’s speeches into three distinct periods of
2000-2009, 2010-2019, and 2020-2021. Here the idea was to capture changes in the
vocabulary over time within a country, most probably caused by changes in topical issues
or political circumstances. The results of this tf-idf analysis
are presented in Figure 3. In
this barplot we can see for each country the most important words in each distinct
period of time. Hence, the Figure 3 highlights temporal changes within each country, but preserves national
differences. In my opinion three central findings from this analysis are worth being
discussed in detail.
First, the connection to topical political issues and circumstances is especially
evident in Germany across the time periods, but also clearly visible in Austrian
speeches of the last two periods. As regards Germany, during the first period
(2000-2009) words like “wiederaufbau” (rebuilding), “betroffen” (upset), and mitgefühl“”
(sympathy, compassion), together with such words like “naturkatastrophe” (natural
disaster), “region” (region), or “erschüttern” (shock, shake) belonging to the top-30
tf-idf words in this period, tackle the 2002 European flood.
Germany was the hardest hit country in Europe and the flood resulted in heated political
discussions in the eve of the Federal elections 2002. In Austrian speeches of this
period words like “jahrhudert” (century), “katastrophe” (catastrophe), and
“hilfsbereitschaft” (helpfulness) document the presence of the 2002 flood – which hit
also Austria quite severely – also in Austrian political discourses.
Another issue in this period was the outbreak of the sovereign debt crisis in the
eurozone in 2008/2009. This economic crisis is present in German speeches through words
like “globalisierung” (globalisation), “wirtschaft” (economy), “arbeitslosigkeit”,
(unemployment). Contrary to Germany, the eurozone crisis is not very present and visible
in Austrian speeches in this period of time. A possible explanation to this difference
might be found in the different roles the two countries played in the early stage of the
eurozone crisis. Germany as the strongest economy in the eurozone played a central,
designing role from the very beginning of the eurozone crisis [
Müller-Brandeck-Bocquet 2010] [
Jsnning and Möller 2016]
[
Kundnani 2016] [
Bulmer 2018], whereas Austria were not strongly hit by, nor a central player in this
crisis. In addition to that I would also stress the economy-oriented political culture
in Germany. The strong role of economic questions as a central shaping factor of German
domestic and European politics increases the weight of topical economic issues in public
debates. Against this background it is not surprisingly that the eurozone crisis
occupied a more significant space in public debates in German compared to Austria [
Allen 2005] [
Maull 2018].
During the second period (2010-2019) the focus of German New Year’s speeches shifts
from economy to refugee politics. In 2015 Chancellor Merkel decided to keep German state
borders open to refugees coming mainly from the Middle East. Although this political
turn was captured in positive political slogans like “Willkommenskultur” (culture of
welcoming) or – most prominently – “Wir schaffen das” (we can do it/ we manage it), the
fact that words like “flüchtling” (refugee), “zusammenhalt” (sticking together),
“herausforderung” (challenge), “außengrenze” (outer border), or “aufnehmen” (receive,
welcome) are among the top-20 terms underline the change the refugee crisis caused in
German public and political discourses during this period of 2010-2019. From the
methodological point of view the identification of terms with a clear connection to the
refugee crisis evidence the usefulness of tf-idf analysis when it
comes to explore discursive changes. Contrary to Germany, the refugee crisis was not
central in Austrian New Year’s addresses. Instead, the national political and economic
setting seem to dominate Austrian addresses. Terms like “marktwirtschaft” (market
economy), “lebensqualität” (quality of life), or “finanz”(finance) tackle economic
consequences of the eurozone crisis. The word “politikverdrossenheit” (jadedness with
politics) refers to domestic political turbulences behind the rise of the right-wing
populist Freedom Party of Austria (FPÖ) in the mid-2010s. Although this rise was partly
supported by the refugee crisis, it was also an expression of disillusionment with the
political elite.
Second, as expected the outbreak of the Covid-19 pandemic disease in Europe in the first
half of 2020 resulted in a clear change, especially as regards the German and Austrian
New Year’s addresses. Words like “pandemie” (pandemic), “impfung”/“impfstoff”
(vaccination), “abstand” (distance), or “corona” (coronavirus) poke from the results as
indicators for this discursive change in political and public discourse in Germany and
Austria. Once again, the results also confirm the usefulness of tf-idf analysis for the exploration of discursive changes through changes in
vocabulary over time.
And third, the overall results confirm the hypothesis that Germany and Austria are, what
comes to political agenda and circumstances, as well to topical issues, closer to each
other than to Switzerland. It is, however, a somewhat odd observation that Swiss
speeches seem to avoid topical political issues and to
concentrate on the construction of neutral narratives. A possible explanation for this
might be found in the fact that in Switzerland, as opposed to Germany and Austria, the
federal president giving the New Year’s speech is not the head of state, but just the
head of Switzerland’s Federal Council, and only carries out some representative duties.
Hence, the federal president is merely a mediator between the different parts of the
Swiss federal state.
The results presented thus far indicate two main aspects. First, New Year’s addresses
offer a valuable and reliable source to explore topical issues and tackle changes in
political and public discourses. And second, although the national political agenda and
circumstances seem to have a stronger impact on the content of the addresses,
significant European political incidents and events, e.g. the refugee crisis or the
Covid-19 pandemic disease, seem to cause changes across countries. This leads us to the
final analysis, an attempt to explore shared topics. For this analysis I constructed
text networks based on bigrams, i.e. word co-occurrences of two adjacent words, and of
skipgrams, i.e. combinations of two non-adjancent words that are three to five words
apart [
Jelveh et al. 2014, 1805]. The bi- and skipgrams were created for
words occurring within the same paragraph. Since the idea is to analyse topics across
countries all word pairs co-occurring in New
Year’s speeches of one single country only were removed. In the last step I calculated a co-occurrence weight measure by dividing the
number of speeches a word pair co-occurs by the total number of speeches (64) in the
multi-national corpus. The interpretation of this weight measure is rather
straightforward: the higher the value, the more significant the word pair for the
cross-country content.
Figure 4 visualises word
co-occurrences network across countries based on bigrams, whereas Figure5 visualises word co-occurrence
network across countries based on skipgrams. Hence, both visualised text networks
present shared word co-occurrences central for the multi-country corpus of German,
Austrian, and Swiss New Year’s addresses, i.e. word pairs that can be interpreted as
fundamental for the – put in Benedict Anderson’s (1991) famous terminology – imagined
discursive community of these three German-speaking countries. The bigram-based core
text network consists of 336 words and 565 co-occurrences, whereas the skipgram-based
core text network consists of 423 words and 1157 co-occurrences.
In order to visually highlight the most important structural properties both
visualisations apply identical visualisation effects:
- Node size is mapped to the node’s degree centrality value and node label size is
mapped the nodes betweenness centrality measure. In network theory centrality, in
general, indicates a node’s position in the network and can be calculated either
relative to a node’s direct neighbours or the whole network. A node’s degree is the
simplest centrality measure and equals to the number of connections the node has to
other nodes. Betweenness, as the term itself indicates, defines centrality by
analysing where a node is placed within the network. Consequently, a node’s
betweenness centrality score is computed by taking into consideration the rest of the
network and by looking at how many times a node sits on the shortest path linking two
other nodes together, thus helping to identify nodes having “a high probability of
occurring on a randomly chosen shortest path between two randomly chosen
vertices” [Hasu and Kao 2013] [Prell 2012, 103–104].
Considering meaning circulation across the entire network, the latter capability is
assumed to be more relevant, since betweenness centrality “shows the variety of
contexts where the word appears, while high degree shows the variety of words next to
which the word appears” [Paranyushkin 2011, 13]. This difference is
important to keep in mind and therefore I use betweenness centrality to measure a
concept’s general status in the text network. As both [Paranyushkin 2011] and [Shim et al. 2015]
point out concepts with high betweenness and degree centrality play a
meaning circulation role across texts in the document corpus, whereas concepts with
low centrality measures are peripheral concepts and, thus, typical only for a certain
part of documents. Between these two extremes are located concepts with high
betweenness but low degree centrality and concepts with low betweenness but high
degree centrality. The former play an important role as bridging concepts between
local communities, the latter, in turn, are local hubs within a cluster [Shim et al. 2015, 59f]. This mapping strategy allows us to easily identify the
most important and influential words in the graph.
- Node colour is mapped to the topic cluster the node belongs to. One of the
proposed key advantages of TNA is bound with the possibility to exploit network
community detection techniques.[8] The underlying idea here is that when textual documents are
reconstructed as text networks a word’s position in the network is not random but
determined relative to the context it appears in. Consequently, words belonging to
the same (or similar) context are assumed to cluster across documents, so that we
could be able to identify groups of words in the network structure having dense
connections within the group, but sparse connections to other
parts of the text next. These groups are here interpreted as topics describing the
main thematic content of the document corpus. By leaning on the promising results of
[Paranyushkin 2011] and [Shim et al. 2015] I applied a modularity-based community
detection method called “Louvain method” based on the assumption, that nodes being
more densely connected together than with the rest of the network construct a network
community [Blondel et al. 2008]. The method identified in total 13
clusters - in my interpretation: topics - in the bigram-based and 12 topics in the
skipgram-based text network.
- Edge width is mapped to the weight of the co-occurrence of the word pair. The more
often two words co-occur in the document corpus, the wider the connecting edge is
visualised in the network graph. Since the graph includes only word pairs shared by
at least two countries, edge effects help us to identify word pairs used together
frequently across countries.
- Layout used for the visualisation is a radar-like centrality layout, in which a
node’s position is determined by its topic cluster and links are arranged according
to their weights. The underlying idea of this layout is to gather nodes belonging to
the same cluster on the same circumference. Further, the visualisation makes also
connections between the clusters – i.e. word pairs, of which words belong to
different topic clusters – easier to identify. As regards the meaning circulation
across the multi-country text network word pairs connecting two topics can be
analysed from the perspective of structural holes. These connections bridging two
separate topics by closing a structural hole in a network can make us aware of
possibly existing latent similarities between these two topics [Burt 2004].
Bigram text network core communities |
(Total # of clusters: 13, modularity: 0.55)
|
Cluster
|
Core content words of the topic(a) |
#1: Solidarity / Sense of community (55 words in
total) |
bürgerin/bürger, frieden, geschichte, hilfbereitschaft, sicherheit, stabilität,
verantwortung, wiederaufbau, zukunft, zusammenhalt |
#2: Crisis recovery (41) |
einsetzen, ereignis, erinnern, freuen, generation, glauben, krise, verbinden,
weltkrieg, überwinden |
#3: Covid-19 era (36) |
erfolgreich, familie, friedlich, meistern, pandemie, phase, resignation,
schwierig, zeit, zusammenleben |
#4: Europe (32) |
entwicklung, erweiterung, europäisch, hoffnung, krieg, mitgliedstaat,
parlament, terror, union, wählen |
Skipgram text network core communities |
(Total # of clusters: 12, modularity: 0.35)
|
Cluster
|
Core content words of the topic(a) |
#1: Crisis recovery (62 words in total) |
erinnerung, friedlich, krieg, mauer, notwendig, optimismus, pandemie,
vergessen, verlust, wirtschaftskrise |
#2: Faith in the future (51) |
aufschwung, bemühen, gestaltung, neugier, selbstvertrauen, technik,
wirklichkeit, wissenschaft, zufriedenheit, zuversicht |
#3: Economy (46) |
arbeitsplatz, beitrag, einsatz, erreichen, global, idee, leisten, offen,
unternehmen, wirtschaft |
#4: Solidarity / Sense of community (45) |
alltag, bürgerin/bürger, demokratie, entwicklung, familie, frieden, gemeinsam,
gemeinschaft, stehen, welt |
Table 1.
Most important topics in the
New Year’s speeches across countries.
(a) Core content words include ten (10) most influental words relative to the topic of
the cluster (listed in alphabetical order). Bold words indicate top ranking words both
in degree and betweenness centrality, i.e. words being central for meaning circulation
across the text network.
Table 1 summarises
the most significant results of the community detection analysis of both the bigram
based and the skipgram based text network by focusing on the four largest communities
found by the Louvain algorithm. As expected there is some variance across topics between
the bigram and skipgram networks. I consider this variation as normal when the
differences in network data creation is taken into account. Actually, against this
background, the two networks should not be considered as exclusionary but complementary
to each other. I have given each community a descriptive label based on a manual
evaluation of the vocabulary linked to the community.
There are two shared topics. I have labelled the first “Solidarity / Sense of community”
and it is the largest topic in the bigram and fourth largest topic in the skipgram text
network. Both includes words “bürger/bürgerin” (citizen) and “frieden” (peace). In the
bigram network this topic is with respect to content more focused on helpfulness and
security, indicated through words like “hilfebereitschaft” (helpfulness), “sicherheit”
(security), “stabilität” (stability), “verantwortung” (responsibility), and
“zusammenhalt” (togetherness, solidarity). The skipgram network, in turn, has in this
topic a stronger bias towards democracy and society embodied by words like “alltag”
(everyday life), “demokratie” (democracy), “familie” (family), and “gemeinschaft”
(community).
This topic appears in different contexts over time. In the German New Year’s speech in
2006 the topic is used to emphasise the inter-linkages between Germany and the
international, global community:
[9]
Deutschland ist keine Insel. Wir stehen in einem internationalen Qualitätswettbewerb,
der alle Bereiche unseres Zusammenlebens betrifft: Welcher Nation gelingt es am besten,
die schöpferischen Kräfte ihrer Menschen zu wecken? Wie offen ist eine Gesellschaft für
Neues? Was bietet sie jungen Familien? In welchem Land gibt es die besten Schulen und
Hochschulen? Wie gut gelingt das Miteinander von Einheimischen und Zuwanderern?
(Germany 2006)
In the German speech of 2015, the topic is used in the context of the refugee crisis to
foster solidarity among citizens, but also to strengthen confidence and commitment to
the community and its values:
Genauso klar ist: Nur mit offenen Diskussionen und Debatten können wir Lösungen finden,
die langfristig Bestand haben und von Mehrheiten getragen werden. Wir sind es, die
Bürger und ihre gewählten Repräsentanten, die entwickeln und verteidigen werden, was
dieses unser liberales und demokratisches Land so lebenswert und liebenswert macht. Wir
sind es, die Lösungen finden werden, die unseren ethischen Normen entsprechen, und den
sozialen Zusammenhalt nicht gefährden. Lösungen, die das Wohlergehen der eigenen Bürger
berücksichtigen, aber nicht die Not der Flüchtlinge vergessen. (Germany 2015)
The Austrian speech of 2007, in turn, highlights the role of persons in different
positions of trust for the security, stability and confidence of the community:
Lassen Sie mich abschließend die Gelegenheit benutzen, um den vielen Frauen und Männern
herzlich zu danken, die in den verschiedensten Funktionen für unser Gemeinwohl, für
unsere Sicherheit, für unsere Gesundheit und für den Zusammenhalt unserer Gesellschaft
buchstäblich oft Tag und Nacht beruflich, oder auch als freiwillige Helferinnen und
Helfer tätig sind. Der Wert dessen, was hier geleistet wird, kann gar nicht hoch genug
eingeschätzt werden. (Austria 2007)
And finally, the following two quotes from the Swiss speeches of 2017 and 2021
establish connections between the economic welfare, sovereignty and stability of the
country and the solidarity and togetherness of the society:
Stabil und erfolgreich wollen wir auch in Zukunft sein. Dazu braucht es mehr denn je
den Zusammenhalt. Unsere Gesellschaft ist stark, weil wir erprobt sind im Versöhnen von
Ansprüchen. (Switzerland 2017)
Wir Schweizerinnen und Schweizer müssen zusammenstehen. Nur so können wir als Land
einstehen für die Interessen von uns allen: für unsere Gesundheit und unser
wirtschaftliches Wohlergehen, für Frieden und Verbundenheit, für Freiheit und
Unabhängigkeit, – kurz: für alles, was uns seit Langem lieb und teuer ist. (Switzerland 2021)
The second shared topic is labelled “Crisis recovery”. The content vocabulary of this
topics indicates that this topic is not limited to the Covid-19 pandemic disease only.
Instead, this topic seems to be used over time to embed a current, topical crisis into a
wider, historical context. Although the clustering algorithm allocates topical words
like “pandemie” (pandemic) or “wirtschaftskrise” (financial crisis) to this cluster, in
my interpretation other content words like “generation” (generation), “krieg /
weltkrieg” (war / world war), “überwinden” (overcome), “optimismus” (optimism), “mauer”
(wall), and “erinnern” (remember) stand for this wider historical context.
Both in the Swiss speech in 2010 and German speech in 2011 this topic was used to
frame the eurozone crisis. Both quotations manifest a clear optimism that the crisis has
been successfully fought and the country will recover rather quickly:
Wir verfügen über eine starke Wirtschaft und eine solide Finanzpolitik. Wir haben es
geschafft, trotz grosser Wirtschaftskrise die Schulden nicht übermässig wachsen zu
lassen. Das wird uns im Aufschwung stärken. (Switzerland 2010)
Deutschland hat die Krise wie kaum ein anderes Land gemeistert. Was wir uns vorgenommen
hatten, das haben wir auch geschafft: Wir sind sogar gestärkt aus der Krise
herausgekommen. Und das ist vor allem Ihr Verdienst, liebe Mitbürgerinnen und
Mitbürger. (Germany 2011)
In 2020, the Austrian speech used this topic in a totally different context combining
the most important crises and challenges of the current time, i.e. climate change,
digitalisation, and migration, as well questions related to gender equality and welfare
state reforms.
Zur Klimakrise kommen weitere große Herausforderungen: Wie werden wir künftig arbeiten?
Welche Antworten geben wir in Österreich auf die Digitalisierung? Wie soll sich unser
Wirtschaftsstandort entwickeln? Wie gehen wir mit Migration um? Und was tun wir, um
Frauenrechte zu stärken? Haben wir ausreichend drüber nachgedacht, das Nötige im
Bildungsbereich anzugehen? Welche Reformen sind im Gesundheits- und Sozialsystem
notwendig, um soziale Sicherheit und sozialen Zusammenhalt für die Zukunft zu
gewährleisten? (Austria 2020)
Both text networks produce also distinct topics. In the bigram network we can identify
the unique topics labelled by myself “Covid-19 era” and “Europe”, in the skipgram
network such topics include “Faith in the future” and “Economy”. As regards the
bigram-specific topics, “Covid-19 era” not only embodies the strenuousness of the
pandemic disease by content words like “pandemie” (pandemic), “resignation”
(resignation), or the word combination “schwierig” + “zeit” (hard times), but also seeks
to create hope through words like “erfolgreich” (successful), “familie” (family),
“meistern” (control), “zusammenleben” (life together). The topic “Europe”, in turn,
tackles both current European issues like terrorism (“terror”), European parliamentary
elections (“wählen” (vote), “parlament” (parliament)) and European integration as a wider
context (“europäisch” + “union” (EU), “erweiterung” (enlargement), “mitgliedstaat” (member
state)). This latter topic clearly evidences the role and status of the EU as the most
important political, economic, and geographical context.
The topic “Covid-19 era” is a mixture of resignation and hope. The Austrian speech in
2021 well exemplifies this mixture:
Ein neues Jahr liegt vor uns. Wir spüren noch die Last des alten, die Last der
Pandemie. Aber viele von uns spüren trotz allem eine hoffnungsfrohe Erwartung, wie sie
nur am Beginn von etwas Neuem stehen kann, wenn alle Möglichkeiten offen und alle Träume
noch frisch sind. (Austria 2021)
The German address, in turn, acknowledges the dramatic changes caused by the Covid-19
pandemic disease. At the same, however, the quotation has a positive trait, stressing
the supportive and stabilising role of the federal state in the times of crisis:
Die Aufgaben, vor die die Pandemie uns stellt, bleiben gewaltig. Bei vielen
Gewerbetreibenden, Arbeitnehmern, Solo-Selbstständigen und Künstlern herrschen
Unsicherheit, ja Existenzangst. Die Bundesregierung hat sie in dieser ganz
unverschuldeten Notlage nicht allein gelassen. Staatliche Unterstützung in nie
dagewesener Höhe hilft. Verbesserte Kurzarbeitsregeln greifen. Arbeitsplätze können so
bewahrt werden. (Germany 2021)
Contrary to Austria and Germany, the Swiss speech of 2021 is marked by negative
ambience, even resignation underlining the deep societal impact the Covid-19 pandemic
disease has caused in Switzerland:
Selten haben wir Vergleichbares erlebt: Unsere Tätigkeiten kamen zum Stillstand. Die
ganze Gesellschaft befand sich in noch nie dagewesener Isolation. Wir mussten lernen,
ohne Händeschütteln auszukommen. Dieses wichtige Begrüssungsritual gefährdete plötzlich
unsere Gesundheit. (Switzerland 2021)
The topic “Europe” is mostly used, as the following quotations exemplify, to remind the
citizens about the fundamental importance of European integration as a uniting community
of Europeans, but also as provider for economic and political security in a global
world:
Es geht nun darum, dass die Union nicht nur wirtschaftlich, sondern auch verstärkt
politisch und emotional zusammenwächst. Die Bürger müssen spüren, dass es sich lohnt, in
diesem erweiterten Europa zu leben und zu arbeiten. (Austria 2003)
In den vergangenen Jahren habe ich oft gesagt, dass es auch Deutschland auf Dauer nur
dann gut geht, wenn es auch Europa gut geht. Denn nur in der Gemeinschaft der
Europäischen Union können wir unsere Werte und Interessen behaupten und Frieden,
Freiheit und Wohlstand sichern. (Germany 2020)
Wirtschaftlich sind wir weltweit verflochten. Geografisch, historisch und kulturell
sind wir ein europäisches Land. Die Beziehungen zu unseren Nachbarländern und zu den
anderen Mitgliedstaaten der Europäischen Union regeln wir in bewährter Weise bilateral.
(Switzerland 2005)
Within the skipgram network, the topic “Faith in the future” represents a strong faith
in technological and scientific development as the most important fundament for a better
future. This is especially evident when we consider such content words like “technik”
(technology), “wissenschaft” (science), “aufschwung” (boom, economic expansion),
“selbstvertrauen” (self-confidence), and “zuversicht” (confidence). Interestingly, this
topic also contains words with strong, dynamic connotation to the shaping the future
(“gestaltung”) and the putting oneself out (“bemühen”). How this topic is used to
generate optimism towards the future is well illustrated by the following quotations:
Wie wichtig es ist, auch im scheinbar größten Durcheinander Gelassenheit, Mut und
Zuversicht zu bewahren. (Austria 2020)
Zusammenhalt, Offenheit, unsere Demokratie und eine starke Wirtschaft, die dem Wohl
aller dient: Das ist es, was mich für unsere Zukunft hier in Deutschland auch am Ende
eines schweren Jahres zuversichtlich sein lässt. (Germany 2017)
Dennoch bin ich zuversichtlich. Denn ich glaube an die Fähigkeit des Menschen zu
gestalten. Es gibt immer einen Weg. Manchmal braucht es Mut und Kraft. (Switzerland 2010)
Finally, the topic “Economy” is characterised by very concrete, economic content words
like “arbeitsplatz” (job, post), “global” (global[isation]), “unternehmen” (company),
and “wirtschaft” (economy). But this topic also has a forward-looking content embodied
by words like “erreichen” (reach), “idee” (idea), or “leisten” (perform). An excellent
example of how this topic is present in the speeches is the following quotation from
the German speech of 2008: “Deutschland kann seine alte Kraft als das Land der Sozialen
Marktwirtschaft wieder neu unter Beweis stellen, der Verbindung von Freiheit und
Gerechtigkeit, Fleiß und Unternehmergeist”. The same spirit can be found in the
following Austrian and Swiss addresses:
Unsere Bevölkerung hat nicht nur ein Recht auf sichere Grenzen und innere Sicherheit,
sie hat auch ein Recht auf ein möglichst hohes Maß an wirtschaftlicher Stabilität und
sozialer Sicherheit. (Austria 2013)
Starke Unternehmen sind der beste Garant für Arbeitsplätze. Und Arbeitsplätze sind die
Basis für soziale Sicherheit und Wohlstand. Noch gehört die Schweiz zu den
wirtschaftlich erfolgreichsten Ländern. Sorgen wir mit freiheitlichen Rahmenbedingungen
dafür, dass das so bleibt. (Switzerland 2016)
Overall, the topics identified by the Louvain method seem appropriate and reliable in
the context of these three countries. The main topics discussed above not only connect
well to the political and economic reality in these countries or in Europe in more
general terms, but also evidence that TNA tools offer – as the previous studies of
[
Paranyushkin 2011], [
Light 2014], or [
Shim et al. 2015] already suggest – a reliable
alternative to traditional methods of text mining. Further, the differences between
topics identified in the bigram and the skipgram based network seem to give support to
the study of [
Jelveh et al. 2014] that the complementary use of both methods can help us
to reliably identify topics that use similar vocabulary but show differences in the text
structure. And finally, it is worth being noted that the most important content words of
each topic contain only a few words with both a high degree centrality and a high
betweenness centrality. This means that the most important content words are those
enjoying higher relevance within the topic and, thus, are descriptive for the topic. A
closer look at words used for meaning circulation across topics and countries reveals
that these words include words related to peace (“frieden”, “friedlich”) and war
(“krieg”), security (“sicherheit”), crisis (“krise”), community (“gemeinschaft”),
economy (“wirtschaft”), and future (“zukunft”). All these “glueing” words are less
topical and more contextual, thus framing and embedding the topical issues into a wider
context of European (integration) history.