∎

11institutetext:  Nico Blokker (✉) 22institutetext: University of Bremen,
Research Center on Inequality and Social Policy
22email<EMAIL_ADDRESS>33institutetext: Andre Blessing, Erenay
Dayanik, Jonas Kuhn, Sebastian Padó, Gabriella Lapesa 44institutetext:
University of Stuttgart, Institute for Natural Language Processing
44email<EMAIL_ADDRESS>
# Between welcome culture and border fence ††thanks: We acknowledge funding by
the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) –
375875969 through MARDY (Modeling Argumentation Dynamics) within SPP RATIO and
by Bundesministerium für Bildung und Forschung (BMBF) through E-DELIB
(Powering up e-deliberation: towards AI-supported moderation).

A dataset on the European refugee crisis in German newspaper reports.

Nico Blokker  André Blessing  Erenay Dayanik  Jonas Kuhn  Sebastian Padó
Gabriella Lapesa

(Received: date / Accepted: date)

###### Abstract

Newspaper reports provide a rich source of information on the unfolding of
public debate on specific policy fields that can serve as basis for inquiry in
political science. Such debates are often triggered by critical events, which
attract public attention and incite the reactions of political actors: crisis
sparks the debate. However, due to the challenges of reliable annotation and
modeling, few large-scale datasets with high-quality annotation are available.

This paper introduces DebateNet2.0, which traces the political discourse on
the European refugee crisis in the German quality newspaper taz during the
year 2015. The core units of our annotation are political claims (requests for
specific actions to be taken within the policy field) and the actors who make
them (politicians, parties, etc.). The contribution of this paper is twofold.
First, we document and release DebateNet2.0 along with its companion R
package, mardyR, guiding the reader through the practical and conceptual
issues related to the annotation of policy debates in newspapers. Second, we
outline and apply a Discourse Network Analysis (DNA) to DebateNet2.0,
comparing two crucial moments of the policy debate on the ‘refugee crisis’:
the migration flux through the Mediterranean in April/May and the one along
the Balkan route in September/October. Besides the released resources and the
case-study, our contribution is also methodological: we talk the reader
through the steps from a newspaper article to a discourse network,
demonstrating that there is not just one discourse network for the German
migration debate, but multiple ones, depending on the topic of interest
(political actors, policy fields, time spans).

###### Keywords:

Discourse Network Analysis Policy Debates Annotation Immigration

##  1 Introduction

#### Text-as-data for Political Claims Analysis.

In recent years, the topic of immigration has increasingly moved into the
focus of the public’s interest. This is a development that is not least driven
by the so called European ‘refugee crisis’ in 2015 that affected citizens at
multiple levels (practically, but also emotionally) and thus kept both law
makers and the media in suspense. Politicians and public figures responded to
the growing numbers of refugees with rapidly changing policy proposals, which
were reported and discussed in the media. Newspapers are an extremely valuable
source for the empirical investigation of the effects of such crises on policy
debates, because of the fine-grained representation they provide, both at the
level of content (extensive, even redundant reports of the positions of
politicians and parties) and at the level of time (multiple articles per day).

Newspaper corpora are therefore one of the primary sources used by text-as-
data approaches within Political Science to study the dynamics of policy
debates in order to understand coalition building and decision-making
processes. Typical research questions are: What are the driving actors and
prominent issues of the debate? Which constellations herald turning points of
the discussion? Will new coalitions emerge or old ones prevail? As these
examples illustrate, the focus of this approach is generally on political
actors and their positions on the specific policy issues (referred to as
political claims in the literature, Koopmans and Statham 1999). Unfortunately,
the text-as-data approach suffers from the lack of available datasets which
are both large-scale and provide high-quality annotation, qualities that are
needed in order to make robust generalizations.

The work reported in this paper evolves around a dataset for this type of
investigation, which we call DebateNet2.0111An earlier version of the dataset
was released in Lapesa et al. (2020) as DEbateNet-mig15, to which we will
refer as DebateNet1.0. and which is released with this paper. It contains high
quality annotation on the topic of immigration in Germany in 2015, based on a
collection of articles from a quality newspaper: die Tageszeitung (taz). The
annotation targets claims (reported demands or propositions) made by political
actors (politicians, parties, organizations, etc.) regarding specific actions
to be taken within the heterogeneous policy field of immigration, covering
aspects of e.g. migration control, foreign policy, integration, solidarity,
racism, and others. The leftmost panel of Figure 1 illustrates an example of
the annotation: the 3 textual spans contain claims attributed to two actors,
Markus Söder and Angela Merkel. In the first span (a) Söder makes two claims:
demanding to fix an upper limits for immigrants (red highlight) and to build a
border fence (purple highlight). In the second span (b), Merkel claims the
opposite regarding the upper limit, and in (c) pushes for a so-called
‘refugees welcome’ policy strategy (green).

Claims in DebateNet are labelled according to an annotation schema (codebook)
defined by political science experts, containing approximately 100 categories
and thus providing an abstract, but nevertheless extremely fine-grained,
representation of the discourse. Such a fine-grained approach to annotation
supports political scientists in gaining a deeper understanding of democratic
decision making in the light of discourse dynamics.

Figure 1: From text to networks: Textual spans containing political claims
translated from articles in the German newspaper taz (left panel) mapped to
the corresponding network representation (right panel). Circles indicate
actors and squares claims. Edges express either support ($+$, blue) or
rejection ($-$, orange).

The fine-grained annotation of actors and their claims described in the
previous paragraph does not constitute an answer to the research questions
about discourse dynamics we mentioned earlier, e.g., actor coalitions, turning
points in the discussion. What is needed is an abstract representation of the
discourse and a methodology to ‘mine’ the pattern of the discourse. We employ
the Discourse Network Analysis (DNA) framework (Leifeld, 2016), a text-as-data
methodology which has become increasingly popular in Political Science that
brings together qualitative discourse analysis (QDA) and network analysis. DNA
builds on the assumption that policy debates can be modeled as a bipartite
network as the one depicted in the right panel of Figure 1. The network
contains two types of nodes: actors (round) and claim categories (square).
Edges between the nodes and the actors encode the fact that the actor made a
statement regarding the specific claim categories, along with the polarity of
this statement. Even if no direct edges are established between actors, or
between claims, this representation allows to explore the status of specific
nodes exactly by looking at its relations to the nodes of the other type. In
our toy example, the upper limit claim is adequately captured as a (contested)
one, as it allows us to contrast the two actors on the stage. One of the
contributions of this paper, along with the release of DebateNet2.0, is a
step-wise description of the workflow that is necessary to go from textual
data to discourse networks.

Along with DebateNet2.0, we also release its companion R package, mardyR222The
name is an acronym for Modeling Argumentation Dynamics of Political Discourse
(in R), which is also the name of the research project within which this
research was carried out (cf. acknowledgments).: it supports both the basic
querying of the annotation (e.g., all the claims made by Angela Merkel on a
certain time span) and the visualization of the discourse networks, allowing
the user to trace policy debates by linking actors to their claims in textual
sources, with the ultimate goal of capturing a comprehensive picture of the
discourse over time. Crucially, we demonstrate the different insights that can
be gained by parametrizing the network at different levels: focusing on actors
vs. on specific claim categories vs. on actor/claims to carry out comparisons
between time slices.

In the second part of the paper, we discuss the results of our DNA case study,
targeted at the characterization of the crisis/debate interplay. In
particular, we focus on two stages of the refugee crisis: Spring 2015, when a
large number of migrants tried to reach Europe via the Mediterranean,
resulting in many lives being lost at sea; fall 2015, when the migration flux
followed the Balkan route, resulting in large number of refugees at the
European borders and increasingly restrictionist migration policies. Beyond
the results of our analysis, this case study also serves as an illustration of
the analysis of DebateNet2.0 with mardyR and as a guideline for the
interpretation of the output of the queries of mardyR on DebateNet2.0.

#### Contributions.

The contributions of this paper comprise multiple levels. At the level of
resources, we document and release DebateNet2.0 and mardyR. DebateNet2.0
addresses the desiderata for a large-scale high-quality policy debate dataset,
namely (a) an annotation schema developed by domain experts, which are capable
of capturing the fine-grained nature of the policy making process; (b) the
availability of annotation layers which allow for a meaningful parametrization
of the queries (e.g., for political actors, mapping of different mentions to a
canonical form, and, ideally, to a party: ‘Die Kanzlerin’, ‘Merkel’
$\rightarrow$ Angela Merkel $\rightarrow$ CDU); (c) the availability of tools
and theoretically motivated case studies which can serve as guidelines for the
visualization and a meaningful interpretation of the results of the query.

At the experimental level, we discuss the results of our case study comparing
the discourse network representations of two crucial stages of the refugee
crisis in Germany.

At the methods level, we provide a step-by-step illustration of the process of
deriving discourse network representations from texts and discuss the
conceptual and practical issues related to this process. Additionally, we
provide the reader with guidelines for the use of our dataset and for the
interpretation of the output of its analysis; such guidelines can and should
be considered as an additional resource we contribute together with
DebateNet2.0 and mardyR.

#### Contextualization.

We believe that our contributions are highly relevant not only for researchers
interested in the immigration topic which is at the focus of our
investigation, but both for the NLP and the Political Science community in
general. From the NLP perspective, the pipeline which leads from raw text to
discourse networks is one which is full of challenges and has thus the
potential to trigger progress in many sub-tasks, most notably the development
of supervised classification methods able to handle fine-grained category sets
in low resource scenarios and the integration of knowledge bases to support
actor mapping. Besides, such a fine-grained debate representation can serve as
a blueprint to structure the understanding of how the same debate unfolds on
less ‘well-behaved’ data and and less traceable actors, as it is the case in
social media.

From the perspective of Political Science, we take a further step in deepening
the understanding of highly dynamic debates on the heels of a (perceived)
crisis. With the help of NLP, the dataset provided here builds the foundation
to scale-up the annotation of political text both with regard to text types
(genres) and scope (topic and volume).

#### Plan of the paper.

The paper is structured as follows: Section 2 provides a background for the
Discourse Network Analysis framework, along with a summary of related work
within the NLP literature. Section 3 describes DebateNet2.0 in all details,
from the selection of the source data, to the annotation, further processing,
and quantitative analysis. Section 4 is dedicated to mardyR. In section 5, we
proceed to our case study: the comparison of the discourse networks from mid
April to May (Mediterranean route) and late September to mid November (Balkan
route). Section 6 concludes the paper by summarizing its findings and
contributions and outlining further modeling work carried out on the dataset.

##  2 Background: Claims Analysis, Discourse Networks, and the Role of NLP

Understanding the structure and evolution of political debates is essential
for understanding democratic decision making, and is therefore of central
interest to political science (de Wilde, 2011; Zürn, 2014; Haunss and Hofmann,
2015).

Democratic decision making can broadly follow two logics, one of which is a
‘technocratic’ mode, where decisions are taken by administrative staff and
field-specific experts. We focus on the second type of decision making, the
‘politicized’ mode, which proceeds through programmatic statements (Schmidt
and Radaelli, 2004) and political debates. While there is no general theory
about mechanisms driving political discourse, there seems to be at least
general agreement that the formation and evolution of discourse coalitions is
a core mechanism (Hajer, 1993; Sabatier and Weible, 2007) and that change in
these coalitions is influenced by external events and by the discourse itself
(Leifeld, 2016).

One promising way to gain insight into such discourse dynamics in an
empirically robust fashion, based on widely available newspaper corpora,
combines political claims analysis (Koopmans and Statham, 1999) with discourse
network analysis (Leifeld and Haunss, 2012). The unit of analysis is the
claim, that is, a demand, proposal, or criticism that is supported or rejected
by an actor (a person or a group of persons) and can be categorized with
regard to its contribution to the debate at hand. Crucially, not all
statements concerning the topic are to be considered a claim, but only those
which target a specific action to be taken (e.g., giving empty flat to
refugees). Claims and the actors who make them are represented together in a
bipartite affiliation network. A discourse coalition is then the projection of
the affiliation network on the actor side, while the projection on the concept
side yields the argumentative clusters present in the debate.

Clearly, manual annotation of such claims and claim-actor relations is a
resource intensive process. It it therefore natural to ask if Natural Language
Processing can help: What are the potentials, limitations, and the practical
issues of applying NLP to the automatic construction of discourse networks?

At a general level, the NLP take on debate modeling can build on the insights
from argumentation mining and subjectivity analysis (Peldszus and Stede, 2013;
Ceron et al., 2014; Swanson et al., 2015; Stab and Gurevych, 2017; Vilares and
He, 2017). An ideal NLP tool would automatically identify the actors and their
contributions to the debate, and analyze such contributions at a structural
level (identifying argumentative structure in their statements), at a semantic
level (classifying statements into relevant categories), and at a pragmatic
level (detecting the polarity of the statements).

Based on our experience (Padó et al., 2019), however, we argue that this task
cannot be completely automated, at least not if the goal is to acquire
representations at the level of (fine) granularity and quality which are
required for the political science analysis. What proved successful is instead
the integration of manual annotation and NLP methods (Blessing et al., 2019)
into a semi-automatic procedure that speeds up the manual work by providing
intelligent proposals, efficient annotation interfaces, and adding automatic
‘pre-annotation’ that is clearly labeled as such. We find that this approach
scales up with manageable loss in fine-grainedness and quality and can serve
as an example of successful ‘mixed methods’ that are becoming more prominent
at the intersection where big data meets humanities and social sciences (Kuhn,
2019).

##  3 DebateNet2.0

In the following sections we introduce the dataset and highlight the steps
undertaken in its creation, from article selection over annotation specifics,
to post-processing, followed by a descriptive analysis. DebateNet2.0 is
available for download as a CLARIN resource (PID:
http://hdl.handle.net/11022/1007-0000-0007-DB07-B).

###  3.1 Source Corpus

The source corpus consists of newspaper articles from the German quality
newspaper die Tageszeitung (taz) spanning the entire year of 2015. We choose
newspaper articles for three main reasons.

First, newspaper articles constitute an elite discourse that affects
democratic decision making (Schneider et al., 2007; Page, 1996): they
aggregate claims of multiple actors while simultaneously filtering content
less relevant to the discussion at hand. We are aware that this gatekeeper
function (White, 1950) of the media is a double-edged sword, as the selection
and the framing of the reported news can vary significantly in relation to the
editorial line of the newspaper (Hagen, 1993), and is also manipulated at
higher levels (agenda-setting). We chose taz, which, while left-leaning, is
nevertheless considered a quality newspaper, meaning that it is reasonable to
assume that claims form central actors from the entire political spectrum are
reported.333We are currently extending the annotation to the more right-
leaning Frankfurter Allgemeine Zeitung (FAZ) in order to compare the
discourses represented in both newspapers. However, because of copyright
restrictions, we will not be able to make the FAZ data publicly available.

Second, newspapers offer a broader overview of the discourse than most other
political texts, such as party manifestos or parliamentary speeches. Hence
they combine partisan, institutional and civic representatives on the level of
individual actors and organizations.

Third, this wide range of political entities enables and requires a fine-
grained analysis of the issue at hand, that translates well into the use of
methods such as Discourse Network Analysis (Leifeld, 2016). Such breadth could
be found also by gathering social media activity of politicians and MPs: the
focus on policy-making would be, however, definitely less sharp and retrieving
the claims would be more challenging (as the journalists both filter and
highlight the relevant information).

###  3.2 Article Selection

The entire corpus contains around 38.000 articles from which roughly 700
contained claims on migration politics in Germany and were therefore selected
and subsequently manually annotated (for more details, see Blessing 2019).
More specifically, we defined a set of seed keywords connected to the topic
(asylum, refugee, migrant/migration, etc.) and German politics. Subsequently,
we trained a binary classifier (Joulin et al., 2016), optimized on recall, on
the articles found in this fashion to identify articles not covered by the
keyword-based approach. Additionally, annotators then flagged false positive
articles for being off topic. An additional criterion was applied to the
subsequently presented dataset: Articles are only included when they contain
at least one political claim. Figure 2 (page 2) illustrates two developments.
Firstly, while the total number of articles published remains relatively
stable over the months (averaging around 3.200 articles, sd = 119) the number
of articles attributed to the topic of migration fluctuates considerably (mean
= 37, sd = 57). Especially between August and December there is a substantial
increase in migration relevant articles, peaking in September. This indicates
that the newspaper placed more relative attention in this time-frame on the
issue of migration at the expense of other topics. Secondly, the difference in
numbers between DebateNet1.0 and DebateNet2.0 appears relatively uniformly
distributed across months. Therefore, the previous release can be seen as a
random sample of the complete dataset, from which similar results might carry
over. Note however that this does not imply that the content of DebateNet1.0
is also a representative sample of DebateNet2.0 (cf. Section 5).

Figure 2 contrasts the total number of articles the taz published in 2015
(divided by 100, right columns) with the number of articles that were deemed
relevant to the migration debate in Germany and annotated accordingly (left
columns). To highlight the differences in numbers between this release of
DebateNet2.0 and the previous one, DebateNet1.0, Lapesa et al. 2020), the
middle column represents the number of relevant articles in the earlier
release.

Figure 2: Number of articles on migration and total articles per month

###  3.3 Annotation

As mentioned in the introduction, the unit of observation consists of textual
entities, so called political claims, and actors (politicians, parties, social
movements, NGOs, intellectuals, etc.). In this section, we discuss the
conceptual and practical issues related to our annotation, spelling out its
different steps. Recall that the components of a discourse network are nodes
(claims and actors) and edges. Our annotation takes raw text as an input, and
aims at identifying these components and characterize them in a multi-layered
fashion. The annotation process is visualized in Figure 3, in panel (1) and
(2). After identifying relevant text spans (1), annotators then attribute both
the corresponding actor(s) as well as choose claim categories, polarities and
date (2). Note that the annotation steps discussed in this section are not
specific to a DNA approach – the additional operations necessary to get to a
DNA are illustrated in section 3.4. The core annotation steps are: claim
identification (step 1) and classification (step 2); date assignment (step 3);
actor identification (step 4); claim attribution (step 5) and polarity
assignment (step 6).

(a) 1\. text snippets; 2. annotations, 3. observations

(b) network (4 actors 4 claims); adapted from (Janning et al., 2009, p. 71)

Figure 3: The annotation process: a) Panel 1 displays four text passages
containing political Claims. Claims and actors are highlighted and coloured.
Panel 2 depicts the resulting annotations actors (circles), claims (squares)
and polarity (plus/minus signs). As shown in b) and c) political claims may
contain more than one actor or claim but no diverging polarity. In Panel 3 the
information encoded within the annotations are pivoted into (un-stacked)
observations with each row encapsulating an actor-claim-polarity triplet. In
b) this information is transformed into an (bipartite) affiliation network
with claims and actors as two types of vertices in the network. Edges are
coloured according to their weight. Dashed and grey lines represent the
projected variants of the then one-mode networks.

Step 1: claim identification. First, the annotators have to identify the
textual spans containing claims. As shown in our examples in Figure 1, claim-
bearing textual spans do not necessarily coincide with a sentence: they can be
a subpart of a sentence, or span beyond the sentence boundary. Discourse
network-wise, this corresponds to the identification of the claim node
(squares in Figure 1). The claim node is further annotated with the document
ID.

Step 2: claim classification. The textual spans identified above are assigned
to one or more claim categories from the annotation scheme (called codebook in
Political Science), which structures the annotation process. Categories and
annotations are moving targets, they traverse a ‘hermeneutic cycle’ during the
annotation process: the aptitude of individual claim-categories is constantly
reviewed, new categories are adopted, outdated categories revised, overlapping
categories merged. All this requires a close cooperation between experts and
trained annotators in order to maximize inter-coder reliability.

Table 1:  High-level categories across annotations major | description | frequency | percentage
---|---|---|---
100 | Controlling Migration | 992 | 22
200 | Residency | 630 | 14
300 | Integration | 386 | 9
400 | Domestic security | 154 | 3
500 | Foreign Policy | 711 | 16
600 | Economy + Labour Market | 153 | 3
700 | Society | 740 | 17
800 | Procedures | 651 | 15

Overall, 110 detailed sub-categories exist that fall into 8 high-level classes
shown in Table 1. Besides fine-grained categories, the codebook also contains
descriptions and defining examples providing guidance to our annotators (an
example is provided in table 8, appendix A); the codebook can be downloaded
from http://hdl.handle.net/11022/1007-0000-0007-DB07-B). Claims are unevenly
distributed across classes paying tribute to foci of the discussion at hand.

The claim nodes identified at the previous step are now characterized by two
annotation layers, namely the fine-grained and the coarse-grained categories.

Step 3: date assignment. the claim is assigned a date, which is by default the
day preceding the publication of the article. It is the annotator’s task to
reconstruct the claim date, based on textual information. The assigned date is
a further layer in the annotation of the claim nodes.

Step 4: actor identification and mapping. The annotators identify the strings
corresponding to actor mentions (e.g., ‘Angela Merkel’, ‘Die Kanzlerin’, ‘Frau
Merkel’). Note that a single claim can be attributed to more than one actor,
and actors can be mentioned inside or outside the textual span. Discourse
network-wise, this step corresponds to the identification of the actor node
(circles in Figure 1). The actor nodes are further annotated with: a) named
entity (PER vs. ORG), b) party444Party affiliations are either directly
annotated, if provided within the newspaper article, or attributed ex post
(either manually or by querying Wikidata)., for the politicians, c) mapping of
the actor mention to a canonical name which serves as a unique identifier of
the actor in the dataset (e.g., ‘Angela Merkel’ for ‘Die Kanzlerin’).

Table 2: actor mapping | pattern | example 1 | example 2
---|---|---|---
1 | string | A. Merkel | mehrere Ministerpräsidenten
2 | spelling | Angelika Merkal | mehrere Ministerprasidenten
3 | parts | Merkel | Ministerpräsidenten
4 | synonym | Kanzlerin | Länder-Chefs
5 | canonical name | Angela Merkel | mehrere Ministerpräsidenten
6 | surrogate | Spokesperson | Spokepersons
7 | pars pro toto | Bundesregierung |
8 | totum pro parte |  | alle Bundesländer
9 | possible name | Bundesregierung | list of all names

Table 2 contains two examples highlighting potential difficulties that may
arise during the process of actor mapping. While Example 1 refers to the actor
by name, Example 2 points to the institutional role/position of the actors
instead. The top half of the table (rows 1-5) displays a set of operations
which fall under the general label of ‘lexical mappings’. Broadly speaking,
the goal of lexical mapping is to find the canonical name of the actor. This
implies unifying different notations, rectifying spelling errors, completing
names, and resolving synonyms. At the end of this process, ‘Angela Merkel’
emerges as the canonical name in Example 1 and ‘multiple prime minister’ in
Example 2. While the former is a proper actor, the latter obviously still
needs to be resolved to individual ministers. But what if we simply cannot
know which prime ministers are being referred to here? In this case, we can
either stick with the somewhat unsatisfying canonical name that we found or
settle on a set of mapping rules. For instance, we could map the prime
ministers as heads of their corresponding states to the group-actor
‘Bundesländer’ representing the states in Germany.

The bottom half of table 2 (rows 6-9) illustrates even more problematic cases,
in which mapping requires assume more complex considerations that are less
easy to formulate in terms of mapping rules. This is the case of spokespersons
or institutional actors that represent another actor. Does the spokesperson of
the federal government also speak for Angela Merkel as the chancellor and
leader of the government? If so, we blur the lexical with the semantic level.
If not, we might lose a lot of claims that are concerned with the daily
business of running a country. On-going work aims to generalize mapping rules
at different levels of granularity depending at the task at hand.

DebateNet2.0 contains only the former type of mapping, i.e. lexical mapping.

Steps 5 and 6: claim attribution and polarity assignment. The claims are now
explicitly linked to the relevant actor. Discourse network-wise, this step
corresponds to establishing an edge between the claim and actor nodes. The
last step is to assign a polarity to the edge: does the actor support or
reject the categorized claim? Polarity assignment is thus performed as an edge
labelling operation.

###  3.4 From texts to discourse networks

In the previous section, we discussed the annotation process and its multiple
layers for actors and claims. A discourse network is not the only way to query
the dataset, obviously: plenty of descriptive statistics and qualitative
insights can be gained from a purely quantitative analysis, which we discuss
in the next section. To get to a discourse network, however, three further
steps need to be performed, which we discuss below.

Step 7: Unstacking. Given that one text-span may consist of multiple
categories and include several actors the format usually required for DNA
needs to be achieved first by un-stacking the annotation. In practice, this
means that for each multi-class claim node previously identified, duplicated
nodes are created which contain an identical span and date, but a single claim
category at a time. The procedure is outlined in Figure 3(a), panel 3.

Step 8: Aggregation. Once the multi-class claims have been unstacked,
aggregation can be performed on the resulting representation, following the
desired criteria which target the different annotation levels. Aggregation is
often performed in multiple steps. The typical first step is to aggregate
across time-windows, and perform a first filtering on the actor/claim pairs
which occur at least m times, a typical value of m being 2. This step builds
on the assumption that, given the redundancy of the reports, hapaxes at an
actor/claim pair can be considered spurious and can be trimmed away without
losing relevant information. The resulting network is referred to as the
‘core’ network (Scott, 2000, p. 112-113), or, more technically, as a m-slice
of the complete network (de Nooy et al., 2005, p. 98). The intuition behind
the core network is to extract only those nodes and edges that are very
central to the debate and filter out potential noise. For purely illustrative
purposes, we display in Figure 4 the 1-slice (raw), 2-slice and 5-slice
network for the same time period (circles are actors, squares are claims). The
1-slice network is extremely dense, contains a high number of actors and
claims that appear at least once, and is therefore often difficult to
interpret. In contrast, in the 5-slice network very little of the structure of
the debate is left (indeed, it is an extreme case that an actor/claim would be
mentioned at least 5 times on different days within the specified time-
window). In the following sections, we will adopt a 2-slice representation,
which generally strikes an appropriate balance between relevant information
and comprehensiveness.

Figure 4: Defining the core network: 1-slice (left), 2-slice (middle), 5-slice
(right)

After the hapax claim/nodes pairs are filtered away on a day-base, typically a
further aggregation step comes into play, which targets the node annotation:
e.g. aggregation on a monthly base, or on the bases of a claim category, or
actor, or a combination of them.

Step 9: Projection. Once the claims have been unstacked and aggregated
according to a desired criterion, yet another level of abstraction may be
applied to capture conceptual similarity: the actor-claim dyad can be
transformed into an actor-actor or claim–claim relation by projecting the
claim nodes on the actor nodes, and the other way around (Leifeld, 2016).
Figure 3(b) (page 3(b)) illustrates how the projection of the actor nodes on
the claim nodes gives rise to the so-called concept-cluster, while the
opposite operation leads to the identification of actor coalitions.

Depending on the research question, distinct follow-up transformations and the
introduction of arbitrary temporal boundaries are feasible (Leifeld, 2016).
Some possible scenarios are covered in section 5. DebateNet 2.0 is in reality
the result of going several times through steps 1 to 9, but could in principle
be archived in one single pass. Section 3.5 provides an overview of some core
text statistics of the dataset.

###  3.5 Descriptive statistics of DebateNet 2.0

In this section we discuss descriptive statistics for DebateNet 2.0, at the
level of actors, claims, and textual spans. This section is thus not concerned
with Discourse Network Analysis, for which we refer the reader to section 5.

The dataset contains 3442 text passages (2. panel Figure 3(a)), which
translate into 4417 individual claims (3. panel Figure 3(a)).555Panel 3 Figure
3(a) actually combines two steps into one: The un-stacking of actors and of
claims. Note, that in the provided dataset the un-stacking of the actors was
already carried out; therefore, the dataset contains 3861 observations at this
interim-step.

#### Actor level statistics.

Table 3 displays the 10 most frequent actors of the entire year.
Unsurprisingly, the most prominent actor of the migration crisis in Germany is
chancellor Angela Merkel, followed by minister of the interior Thomas de
Maizière, and, as institutional actor, the federal government
(‘Bundesregierung’). Other relevant actors include Merkels’ political
antagonist in the migration debate Horst Seehofer and chairman of the
Christian Democratic Union (CSU), the Foreign Minister Sigmar Gabriel, and
also the President of the European Commission Jean-Claude Juncker.

Table 3: Frequent actors in the migration debate 2015 (after actor mapping)
Actors
---
name | freq
Angela Merkel | 247
Thomas de Maizière | 162
Bundesregierung | 152
CSU | 86
Horst Seehofer | 79
SPD | 78
EU | 77
Sigmar Gabriel | 68
Grüne | 60
Jean-Claude Juncker | 57

#### Claim-level statistics.

Recall that Table 1 (page 1) describes the distribution of higher-level-
categories across the dataset. The most frequent category is ‘controlling
migration’, which contains demands and proposals concerned with regulating
immigration (border controls, upper limit, asylum law, etc.). Related and also
prominent is ‘foreign policy’ (e.g. EU-wide quota, international solutions).
Other categories are ‘society’ that deals with humanitarian and cultural
aspects (human rights, Christian values) and ‘residency’, mostly concerned
with the accommodation of migrants. Least frequent are ‘domestic security’ and
‘economy + labour market’ which are further downstream of the acute
(perceived) crisis situation. Although, it stands to reason that both gained
momentum after 2015 and are more present during the 2016 discussion (not
covered in our dataset). A special, less topical category is ‘procedures’ that
often appears in combination with other categories (additional funding,
transparency, etc.). Table 4 and 5 display the most frequent positive and
negative sub-categories of 2015, respectively. EU-Solution (501), such as a
European-wide quota for refugees, is the most often used claim with a positive
polarity. Followed by calls for more funding (805) and an upper limit (102).
Oppositely, frequent claim categories with negative polarity are calls to
oppose xenophobia (703), right wing radicalism (709), and the current
immigration policies (190). An EU solution is also high on the list,
indicating that this is a polarizing and contested claim category.

Table 4: Frequent Positive Claims Positive Claims
---
Code | Claim Category | Frequency | Total (incl. neg. Claims)
501 | EU solution (quotas for refugees) | 237 | 310
805 | additional financing | 147 | 155
102 | ceiling/upper limit | 129 | 152
812 | fast / accelerated procedure | 124 | 132
207 | deportations | 112 | 140
504 | safe country of origin | 112 | 153
105 | border controls | 103 | 126
705 | refugees welcome | 94 | 107
309 | care (medical, financial, …) | 87 | 119
104 | isolation/immigration stop | 86 | 128
Table 5: Frequent Negative Claims Negative Claims
---
Code | Claim Category | Frequency | Total (incl. pos. Claims)
703 | xenophobia | 129 | 161
709 | right-wing radicalism | 86 | 98
190 | current migration policy | 73 | 95
501 | EU solution (quotas for refugees) | 73 | 310
202 | refugee accommodation | 56 | 79
110 | asylum law | 49 | 98
711 | islam | 47 | 63
104 | isolation/immigration stop | 42 | 128
504 | safe country of origin | 41 | 153
401 | violence against migrants | 36 | 44
Figure 5: Timeline of the ‘refugee crisis’ in Germany.

#### Timeline.

Before moving to DNA, we showcase the number of resulting claims over the
course of 2015 in Figure 5. The graph displays the weekly number of claims in
2015 roughly divisioned into time periods named after prominent events
(modified after Haller 2017)666Haller identifies 10 pertinent events and
investigates how they were conveyed in leading German newspapers. Here, we
adopted some parts and labels of the detailed classification of this study and
transferred them to a much coarser categorization. For a brief summary of each
time period refer to appendix B (page B).777Labeled dates symbolizing micro-
events are constructed following
https://en.wikipedia.org/wiki/Timeline_of_the_European_migrant_crisis#2015 and
https://www.sueddeutsche.de/politik/migration-etappen-der-fluechtlingskrise-
in-deutschland-eine-chronologie-dpa.urn-newsml-dpa-
com-20090101-160831-99-279450. This is not meant to be a full-fledged
analysis, but a rough classification that guides future examinations. As
foreshadowed by Figure 2 (page 2), claims are unevenly distributed throughout
the year, with distinct peaks in September and October.

Each phase transition is a natural way to separate the debate into fragments
that in turn can be analyzed in depth separately or in combination with other
periods. For instance, with the numbers of refugees trying to escape the war
in Syria via the Mediterranean the discussion about the necessity of sea
rescues and efforts to fight smugglers flare up in April. A debate less
critical by the time the ‘Balkan Route’ gains prominence. Obviously, there is
causal connection between relocating the points of entry into the European
Union but the focus of the discussion has shifted to other policies to address
the new developments (e.g. the Hungarian border fence ‘Grenzzaun’).

#### Text-level statistics: keyword analysis.

An alternative way to explore and analyse the data provided in DebateNet2.0 is
using keywords. This approach builds on the assumption that changes in
policies are accompanied by a different vocabulary to express them. In other
words, are these changes reflected in a change of word distributions? To
extract the keywords, we used the YAKE system (Campos et al., 2018) with
standard settings. YAKE’s keyword extraction approach combines word frequency
information with finer-grained features related to the position of a candidate
keyword in the sentences in which it occurs, as well as with dispersion scores
(at the sentence level and at the level of the different contexts of
occurrence). For each annotated text passage, we extracted two keywords with
YAKE. We then aggregate the keywords per month, ranking them according to
frequency. Table 6 (page 6) displays the most frequent keywords for the month
April (representing the Mediterranean period) and October (representing the
Balkan route period). As expected, the word ‘Seenotrettung’ (emergency sea
rescue) and ‘Schlepper’ (smugglers) are most frequent in April along with
‘Bundesregierung’ (federal government). The latter also occurs frequently in
October, together with all governmental parties (CDU/CSU and SPD) and high
ranking government officials. As for claims, keywords indicating
restrictionist policies, such as ‘grenze/grenzen’ (border), ‘transitzonen’
(transit zones), and ‘abschottung’ (isolation/walls-up) rank high in October.

Table 6:  Most frequent keywords in April and October. April | October
---|---
keyword | freq | keyword | freq
bundesregierung (fed. government) | 8 | spd (SPD) | 20
seenotrettung (sea rescue) | 6 | flüchtlinge (refugees) | 19
cdu (CDU) | 5 | cdu (CDU) | 18
maizière (Maizière) | 5 | merkel (Merkel) | 17
europa (Europe) | 4 | deutschland (Germany) | 13
schlepper (smugglers) | 4 | csu (CSU) | 12
spd (SPD) | 4 | bundesregierung (fed. government) | 11
asylbewerbern (asylum seekers) | 3 | regierung (government) | 10
aktivisten (activists) | 2 | flüchtlingen (refugees) | 9
asyl (asylum) | 2 | seehofer (Seehofer) | 8
behandlung (treatment) | 2 | transitzonen (transit zones) | 8
bitte (request) | 2 | grenze (Border) | 7
bleiberecht (Right of residence) | 2 | oktober (October) | 7
bundesamt (federal office) | 2 | abschottung (isolation) | 6
bundesländern (states) | 2 | grenzen (borders) | 6
bündnis (alliance) | 2 | kanzlerin (chancellor) | 6
fähren (ferries) | 2 | land (country) | 6
flüchtlinge (refugees) | 2 | maizière (Maizière) | 6
rettungsmission (rescue mission) | 2 | türkei (Turkey) | 6
a For October we present all keywords with a frequency above 5.
b In April this threshold is 2.
Note, that we hand-picked the keywords in case there was a tie in frequencies.

These findings demonstrate that the keyword lists can provide a general idea
about the topics of discussion and the most central actors involved. Yet, it
is not possible to make inferences regarding the relation between different
actors and claims on this basis. This motivates our use, in the next sections,
of discourse networks. We expect that they not only mirror these developments,
but also shed some light onto relational aspects and the decision making along
the process with it.

Figure 6: mardyR - dashboard

##  4 Visualization with mardyR

In order to quickly inspect the dataset and replicate the post-processing
steps described in Section 3, as well as to obtain a visualization of
discourse networks (Section 5), we developed a companion software package in
R, mardyR. The package offers an interactive, browser-based web application in
the form of a dashboard built around the shiny888https://shiny.rstudio.com/
and https://rstudio.github.io/shinydashboard/ framework (Chang et al., 2020;
Chang and Borges Ribeiro, 2018)999The app and a brief manual can be found
here: http://hdl.handle.net/11022/1007-0000-0007-DB07-B.

Actor-level aggregation. For demonstration purposes, we use this application
to trace the involvement of chancellor Angela Merkel over the course of the
debate. We selected the time-windows from Figure 5, corresponding to
‘Mediterranean’ and ‘Limit Immigration’). Figure 6 (page 6) displays all (un-
stacked) claims by Merkel as reported in the taz from 15th April to 31st May.
The entries of the dataset are presented in the right panel of Figure 6 while
the left panel contains configuration options, e.g. select variables, apply
time-frame, choose which release of the dataset to use. In this period, she
stresses the importance of humanitarian rights (702) and proposes to fight
smugglers (twice, 109) as well to combat causes of flight (503). The
corresponding network is displayed in Figure 7 (page 7), with Merkel as round
node surrounded by and linked to three square nodes representing claim-
categories. Overall, the resulting network consists of four nodes and four
edges with an average degree centrality of 2 (measured as the average number
of edges connected to a node, Wasserman and Faust 1994, p. 100).

Figure 7: Spring: Ego-network Merkel Figure 8: Fall: Ego-Network Merkel

In contrast, Figure 8 (page 8) makes it apparent how much more involved she
became during the later period of the year from 28th September to 15th
November. Suddenly, 96 claims made by Merkel are reported in the newspaper,
which translate into 32 distinct claim categories she either supports (blue
edges) or rejects (red edges).

Claim-level aggregation: Similarly, one can trace the usage of different claim
categories over time or cross-sectionally. In Figure 9 (page 9) all instances
of the claim ‘sea rescue’ (111) from the first time period are displayed and
linked to the corresponding actors (colours indicate party affiliation). The
overwhelming majority supports the claim of sea rescue missions. Therefore,
from a purely structural standpoint, it is even more surprising that this
claim does not appear in the second time period at all. Instead of 111 other
claims have emerged and gained popularity.

Figure 9: Spring: Ego network sea rescue

Overall, this goes to show the dyadic relation between actors and claims and
vice versa. However, the real strength of social networks only comes to light
when the whole network with all its interrelations is considered
simultaneously. Only then it is possible to capture the essence of the
debate.101010The current version does not support external datasets. However,
we plan to update a future version with this functionality. Prerequisite for
this is a similar data format (actor - claim - polarity).

##  5 Mediterranean vs. Balkan Route: a Discourse Network Analysis

From the combination of claims of different political actors a network
structure emerges, whose analysis brings its own challenges and chances. On
the one hand, the analysis becomes more demanding due to the increased
complexity of the task, imposed by inherently existing inter-dependencies
(actors may influence each other in their decision to oppose or support
claims). On the other hand, complexity enables a new relational perspective on
the discussion. For instance, it permits the researcher to trace how
coalitions and alliances are formed or which actors reach out and bridge
between hardened fronts.

We exemplify the use case of discourse network analysis by contrasting two
distinct time periods of the 2015 debate in this section: the ‘Mediterranean’
versus the later stages of the ‘Balkan route’ (Figure 5 on page 5, ‘limiting
immigration’). Firstly, this allows us not only to assess who the driving
actors and claims of the discussion at certain time-points are, but, more
importantly, also how they relate to each other and form (dynamic) coalitions.
Secondly, we compare the networks obtained by using a) the entire dataset of
2015 (DebateNet2.0) and b) a previously released subset of the data
(DebateNet1.0), in order to evaluate how important the size of the dataset is
to understand the migration debate at hand.

Table 7: DebateNet2.0 over time month | # claims | # unique categories | # unique actors | average degree
---|---|---|---|---
January | 403 | 61 | 160 | 2.78
February | 163 | 46 | 79 | 2.14
March | 160 | 52 | 75 | 2.13
April | 177 | 47 | 76 | 2.37
May | 179 | 43 | 65 | 2.37
June | 115 | 44 | 59 | 1.92
July | 223 | 54 | 95 | 2.34
August | 449 | 64 | 175 | 2.90
September | 910 | 90 | 247 | 3.71
October | 743 | 78 | 239 | 3.53
November | 565 | 80 | 164 | 3.23
December | 330 | 69 | 109 | 2.76

#### Network statistics.

A first step in the analysis is to describe the network properties on a basic
level. Table 7 depicts the number of claim frequencies (second column), the
number of unique claim categories (third column), the number of unique actors
(fourth column), and the average degree on a monthly basis (last column).

Unsurprisingly, the fluctuating number of claims matches our previous
observations (Figure 2, page 2 and Figure 5, page 5). While September is the
month with the most observations, actors and claims, as well as the highest
average degree, June operates on a much lower level. Therefore, the intensity
of the debate plummets in June, only to increase drastically from August to
September. Afterwards it plateaus on a high level until the end of November.

In the following, we once more focus on the time period discussing sea rescue
missions in the Mediterranean from 13th April to 31st of May and contrast it
with the debate about limiting migration from 28th September to 15th November
in conjunction with the Balkan route, which ushers in more restrictionist
migration policies compared to the preceding ‘Wir schaffen das’ (we can do it)
period (see Figure 5, page 5). Additionally, we compare the resulting networks
to their counterparts built from the dataset of the previous release with much
less observations (DebateNet1.0). Recall that this resembles a random sample
and its distribution closely follows the current one.

Figure 10: 2-slice networks of DebateNet2.0 (left) and DebateNet1.0 (right)
from mid April - May

#### In the spring,

the core network represents a discussion largely at the EU-level (Figure 10,
page 10): The demand to introduce an EU-wide solution (501), possibly a quota
for the distribution of refugees, is not only at a very central position in
terms of degree but also exhibits the highest betweenness-centrality (measure
of how many shortest paths are passing through this node, Freeman 1978). Here,
it has a brokerage position, bridging two otherwise disconnected components of
the network. Similarly prominent is Thomas de Maizière, the minister of the
interior, who has a conflicted position with respect to the claim of sea
rescue missions (as indicated by the grey and red arc) but supports a EU-wide
solution. As does Jean-Claude Juncker and Ska Keller (MP of the Greens).
Another relevant claim is that of fighting against people smugglers (109) that
finds support from Great Britain (Großbritannien), the EU, and Federica
Mogherini (High Representative of the Union for Foreign Affairs and Security
Policy of the EU). In the scope of the discussion about fighting against
smugglers it was also suggested to launch a military operation to destroy
their boats on the coast of Libya, hence the closed fourcycle with category
508 (military intervention). The national level plays only a small part in the
periphery of the network: neo-fascists (Neonazis) opposing Islam (711) which
ties in with the Islamophobic Pegida (Patriotic Europeans Against the
Islamisation of the Occident) movement. Overall, these observations concur
nicely with our expectations from the keywords-analysis and proposed time-
period: Both topic and actors appear to be largely congruent across methods.

If compared to the network built from DebateNet1.0 (right panel Figure 10),
the vulnerability of networks (Kossinets, 2006) becomes visible: The core
network observed for the complete dataset (left panel, Debatenet2.0) gets
fragmented into components that are no longer connected to each other. By
looking at the right panel one might assume that the actors appear not to talk
about the same policies at all. This is of course a question of aggregation
and the time windows used. Yet it highlights the importance of a comprehensive
analysis: If the task is to analyse the core network of the debate, then a
random sample would not suffice in this case.

Figure 11: 2-slice networks of DebateNet2.0 (left) and DebateNet1.0 (right)
from late September - mid November

#### In the fall,

this fact becomes even more apparent (Figure 11, page 11). In the left panel,
three overlapping clusters within the debate can be observed. The first and
largest cluster dedicates itself to questions of residency and integration and
involves mostly governmental actors and parties. Arguably, this cluster
contains two separate discussions as indicated by the projection onto actor
coalitions (Figure 12, page 12); namely one focusing on deportations (207) and
an upper limit (102) and another discussing so called transit zones (215) and
safe countries of origin (504) in which deportations are permitted. While
deportations and limits regulate the number of refugees allowed into Germany,
transit zones aim to deter applicants by restricting their movement. The
second cluster contains the far right movement (e.g. the Alternative for
Germany (AfD) and the Pegida organization) and their nationalist claims
regarding isolation (104), their expression against the current migration
policies (190), and xenophobic and Islamophobic slogans. The third cluster
combines actors from the EU and the national level with claims regarding an
EU-wide solution (501) via quotas or the readmission-pact with Turkey (507) as
well as border controls (105). As foreshadowed by Figure 8, Merkel has an
important bridging function between the different clusters by being connected
to a lot of different claims from different clusters in this time period.

However, once we only consider data from the DebateNet1.0, Merkel gets
separated from the giant component and the second and third clusters
disappear. This indicates that a random sampling strategy is unsuitable for
the, admittedly highly specific, task of extracting a core network of the
migration debate. Nevertheless, the sample is by no means unusable. Instead,
it highlights the importance of task-dependency. For example, if the goal is
to identify a list of relevant claim categories during the time period from
28th September to 15th November, then even the sample might prove sufficient:
Almost 80 percent of distinct claim categories existent in DebateNet2.0 are
already present in DebateNet1.0 (74 out of 95). This number increases to 95
percent if the entire year is considered.

Figure 12: First row: Projection of 2-slice network of fall 2015 (Figure 11).
Second row: Projections sorted by polarity. Left: Positive congruence network
(pos-network), middle: negative congruence network (neg-network), right:
conflict network

#### One-mode Projections

of the actors are another way to look at the fall-network that capture co-
occurrences within the data (Leifeld, 2016). Actors are linked to each other
if they share the same claims and hence form coalitions based on the topical
similarity of their political interests (1st row of Figure 12). Depending on
their polarity with regard to the claims in question, the relation may either
reflect mutual support, shared disagreement, or conflicting stances (2nd row
of Figure 12). Conceptually, this represents yet another level of abstraction
from two-mode representation and its underlying textual information. Arguably,
this makes it easier to identify clusters within the network:

A basic community detection algorithm based on (greedy) modularity
optimization (Newman, 2004; Clauset et al., 2004) suggests four co-existing
clusters (communities) within the actor-network (coloured ellipsis in the
first panel). The aim of the algorithm, belonging to the family of
(agglomerative) hierarchical clustering procedures, is to identify groups of
nodes with higher density between them than between nodes of different groups,
a property called modularity. At the start, each node is placed in its own
cluster and then iteratively merged with neighbouring clusters if the
conjunction increases modularity. The clusters are mostly consistent with the
observations made above on the two-mode level: Governmental actors (red,
black, and light-blue nodes) are mostly distributed into two clusters (limit
and deter, pink and green). European actors are centered around yet another
cluster (EU, brown). The last cluster is defined by the debate about
fundamental questions of migration by far right actors (far right, violet).
The projection approach highlights how dynamic actor coalitions present
themselves as the discourse progresses. For instance, Thomas de Maizière,
minister of the interior, now focuses more on domestic policies than he did
during the sea rescue discussion amidst the EU compound earlier in the year.
Moreover, he is densely embedded into the governmental coalition, representing
the building of consensus and thus facilitating more decisive actions within
the executive. Similarly, Angela Merkel has to defend her earlier ‘Wir
schaffen das’ against propositions by the far right AfD that ultimately would
become the biggest opposition party in the 2017 election in Germany.

The benefit of the network-based approach becomes particularly evident by
looking at the different kinds of relations expressed in Figure 12. Without
the network perspective, e.g. by looking only at keywords, it would be
difficult to infer how different actors relate to each other. This is not to
say that keywords do not provide valuable insights on their own. On the
contrary, as shown in Table 6 (page 6), relevant actors and claims correspond
highly to prominent nodes found in the network (e.g. Thomas de Maizière). In
practice, one could initially deploy keyword analysis in order to get a
general understanding of the most prominent actors and pressing issues of the
debate, followed up by a more detailed DNA approach.

##  6 Conclusion

In this paper, we have introduced Debatenet2.0, a dataset containing expert
annotation on the German political debate on the so-called ‘refugee crisis’ in
2015. The core units of our annotations are political claims (e.g., ‘quotas
for refugees should be established’) and the actors who made those claims
(e.g., ‘Angela Merkel’, ‘the government’, ‘the protesters’). While
Debatenet2.0 naturally lends itself to several quantitative investigation
approaches (e.g., keyword analysis), its primary purpose is to allow for a
Discourse Network Analysis of the debate. Indeed, taking the reader on a tour
of the basics of DNA, both conceptually and practically, is one goal of this
paper. Our contributions comprise multiple levels. At the level of resources,
we release Debatenet2.0 and its companion R package mardyR, which allows the
user to create independent queries and visualizations. At the methodological
level, we provide a step-by-step explanation of the task of deriving discourse
networks from textual data, helping the reader to bridge the conceptual gap
between the two fields involved in this interdisciplinary research (Political
Science and Computational Linguistics). A further contribution is the case
study, in which we address the research questions of a) how specific, concrete
events in the crisis affected the discourse network representation of the
policy debate and b) how data-dependent the discourse network representation
is (we compare networks built from the full dataset to those of a random
subsample of it). Broadly, we demonstrate how the debate gained momentum by
comparing the spring network with the fall network and we highlight the
formation of actor coalitions. Moreover, the vulnerability of the core-network
in the face of incomplete (sampled) data is addressed. Beyond answering the
topical research questions, our case study serves also as an illustration of
the application of DNA, and can serve as a guideline for the formulation of
research questions on Debatenet2.0 (and comparable datasets) and for the
interpretation of the results.

Beyond its use to address DNA research questions, Debatenet2.0 also has a
clear potential for NLP, which we have only hinted at in the introduction and
background section. As a matter of fact, further NLP modeling has been
conducted within MARDY, beyond claim detection and classification (Padó et
al., 2019). This work has targeted the two core ingredients of the network. On
the actor nodes, (Dayanik and Padó, 2020) have improved the robustness of the
claim identification by targeting the frequency bias issue: claim
identification (and thus any representation built on it) is fair only if low-
frequency actors get identified correctly. On the claim nodes, ongoing
experiments test different modeling architectures capable of correctly
classifying the fine-grained categories encoded in the codebook (recall,
approximately 110); note that this research goal is in a sense parallel to the
strive to be fair to low-frequency actors, as fine-grained categories often
lead to low-frequency issues which in turn affect robustness over time: as
shown in our experiments, specific historical events trigger and shape the
debate, making a claim infrequent at time $t$ (the training data) extremely
relevant at time $t+1$ (on which we want to apply the classifier).

From a Political Science perspective, further work has targeted the evaluation
of the discourse network extracted with a NLP claim identifier, in a direct
comparison to the network extracted with the expert annotation; Haunss et al.
(2020) found that, due to the natural redundancy present in newspaper
articles, the 2-slice network derived from the claims identified automatically
(with a manual filter on false positives) is equivalent to the one developed
from fully manual annotation. Besides, they also show that integrating
automatic claim identification in the annotation environment would improve
inter-annotator agreement, ensuring a more reliable annotation. As far as the
robustness of the immigration codebook is concerned, Blokker et al. (2020)
successfully expanded the domain of its application to party manifestos, which
contain an ‘officially approved’ representation of the position of a party
with respect to specific policy issues, and one which lends itself naturally
to a comparison to the individual positions of the political actors which are
depicted in the newspaper.

Current work on Debatenet2.0 adds annotation of articles from 2005 and 2010,
and annotation of a new topic, namely the pension debate, to the dataset. At
the level of annotation layers, we plan to introduce an additional node type,
the so-called frame node, which encodes the reason provided by the actor to
support a claim (e.g., a border fence should not be built because it violates
human rights). From a NLP perspective, the introduction of the frame nodes
will bring in new challenges closely related to those tackled by the Argument
Mining community (Cabrio and Villata, 2018; Eger et al., 2018), thus
strengthening the interdisciplinary potential of the research agenda behind
Debatenet2.0.

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##  Appendix A Annotation guidelines: example

102: Limit immigration/upper limit • Explanation: This claim refers both to
limiting the number of refugees by means of a fixed maximum number and to
unspecified demands for a reduction in the number of refugees. • Example:
‘There will be no way around a limit and thus an upper limit for immigration.’
---
111: sea rescue • Explanation: This claim refers to demands for the rescue of
refugees in distress at sea. • Example: ‘The human rights organisation is
calling on the EU to set up a joint sea rescue operation on the Mediterranean
see off the Libyan coast and to create significantly more admission places for
refugees.’
504: Safe legal status for country of origin • Explanation: This claim refers
to demands for the extension of the legal status of one or more countries of
origin of refugees as ‘safe’. • Example: ‘In addition, the legislator must
declare more Balkan countries safe third countries “to which we can then
deport more quickly”.’
Table 8: Fine-grained categories: codebook examples and annotation guidelines

##  Appendix B Timeline

  1. a)

Pegida (01/01/2015 - 15/02/2015): This time period is characterised by claims
made around the Islamophobic Pegida (Patriotic Europeans Against the
Islamisation of the Occident) movement that was founded in October of 2014.
Prominent claims and points of discussion of this period are Xenophobia,
Islam, and Public Debate (should politicians engage with the movement or
categorically refuse communication?).

  2. b)

Tröglitz (16/02/2015 - 12/04/2015): This period revolves around the town of
Tröglitz in Germany and the proposal of its mayor to accommodate refugees,
inciting (violent) protests and threats by far-right sympathisers and
subsequently compelling the mayor to resign. Prominent claims and points of
discussion of this period are refugee accommodation and violence against
migrants.

  3. c)

Mediterranean (13/04/2015 - 31/05/2015): During this period, the media
increasingly reported on refugees trying to cross the Mediterranean by boat
under high casualties. The EU holds an emergency summit and subsequently
proposes a quota to distribute the incoming refugees. Prominent claims and
points of discussion of this period are EU-solution, emergency sea rescue, and
fight against people smugglers.

  4. d)

refugee trail (01/06/2015 - 01/08/2015): While refugees are still trying to
reach Europe from the sea, another route increasingly gains the attention of
the media: the Balkan route. Hungary starts to build a border fence to fend
off asylum seekers. Still, refugees are reaching Germany invalidating the
Dublin-III regulation. Prominent claims and points of discussion of this
period are (medical and financial) care for refugees, safe countries of
origin, and an immigration law.

  5. e)

Heidenau (02/08/2015 - 30/08/2015): As a reaction to a planned refugee
shelter, the extreme right and its sympathisers riot and refuse access to the
shelter. Emerging claims and points of discussion of this period are right-
wing extrism, (accelerated) deportations, and Dublin-regulation.

  6. f)

‘We can do it’ (31/08/2015 - 27/09/2015): Angela Merkel declares on 31th
August ‘Wir schaffen das’, Germany and Austria open their borders (4th
September) only to temporary enforce border controls again later that month.
Prominent claims and points of discussion of this period are EU-solution,
refugees welcome, and border controls.

  7. g)

limit immigration (28/09/2015 - 15/11/2015): The ‘refugees welcome’ enthusiasm
is followed by a tightening of asylum restrictions and an increasingly tense
climate. Prominent claims and points of discussion of this period are upper-
limit, walls-up policy, borders controls, and the cooperation with transit
countries.

  8. h)

international solutions (16/11/2015 - 31/12/2015): This period largely
continues the policy of the previous phase with an slight increase in claims
regarding foreign policies (the G20-summit took place in Antalya on 15/16.
November). Prominent claims are EU-Solution, cooperation with transit
countries, and combat causes of flight.

For a more in-depth analysis of the events and the accompanying media-echo
refer to (Haller, 2017).