=Paper=
{{Paper
|id=Vol-2877/paper5
|storemode=property
|title=Using Heterogeneous Information Networks for Integrative Discourse Mapping
|pdfUrl=https://ceur-ws.org/Vol-2877/paper5.pdf
|volume=Vol-2877
|authors=Alexander Brand,Tim König,Wolf J. Schünemann
|dblpUrl=https://dblp.org/rec/conf/www/BrandKS21
}}
==Using Heterogeneous Information Networks for Integrative Discourse Mapping==
https://ceur-ws.org/Vol-2877/paper5.pdf
Using heterogenous information networks for integrative discourse
mapping
The Covid19 example
Alexander Brand Tim König Wolf J. Schünemann
Institute of Social Sciences Institute of Social Sciences Institute of Social Sciences
University of Hildesheim University of Hildesheim University of Hildesheim
Hildesheim, Germany Hildesheim, Germany Hildesheim, Germany
alexander.brand@uni- tim.koenig@uni-hildesheim.de wolf.schuenemann@uni-
hildesheim.de hildesheim.de
ABSTRACT extension in order to live up to the newly accessible data
worlds of digital societies. One of the generic operations of
This short paper presents a novel way of mapping knowledge discourse analysis, and quite often the best way to reduce the
communities in discourse by utilizing heterogenous
complexity of results, is discourse mapping. Discourse mapping
information networks (HINs) and a two-stage grouping
procedure. After laying out the theoretical foundations of a is to be understood as an umbrella term for visualisation
discourse analytical framework grounded in the sociology of techniques that allow for synoptical integration of relational or
knowledge, it will demonstrate the applicability of the knowledge structures dissected through discourse analysis.
framework on the platform Twitter. In exploratively analysing The short paper puts emphasis on this particular task.
a sample of 6.317.324 tweets on the Covid19 pandemic, we will
show how clustered HINs can make visible the social Qualitative discourse research and other approaches within
embeddedness of knowledge production in digital the interpretive paradigm of social sciences have developed a
environments. great multitude and rich variety of mapping strategies for
illustrative and instructive syntheses of empirical findings [2,
3, 4, 5]. Such maps can be regarded as a type of small-sized,
CCS CONCEPTS non-standardised, interpretation-loaded knowledge graphs,
•Applied computing~Law, social and behavioral making visible the heterogenous knowledge communities that
sciences~Sociology•Information systems~Information shape discourses. Established discourse mapping strategies,
systems applications~Data mining~Clustering though highly flexible, cannot easily cope with the large-scale
data of online communication and their need for
KEYWORDS standardisation and automation. Due to their requirements in
interpretive work, they are not scalable, thus not extendable to
Network Analysis, Covid19, Heterogenous Information Networks,
wider contexts of meaning-making or transferable to other
Discourse Analysis, Sociology of Knowledge
subjects. In this paper, we argue that the flaws in established
mapping strategies can be - at least partly - overcome by using
HINs for the integrative and adaptive mapping of discourses.
1 Introduction: Scalable and adaptive HINs are defined as a directed graph consisting of multiple
mapping tools for discourse analysis types of objects or multiple types of relations between objects
In this short paper, we present ongoing work that seeks to [6]. This mirrors the assumptions of discourse maps, which
combine the theoretical foundations of knowledge-oriented relate different actors to different kinds of information in order
discourse analysis with the application of heterogenous to make visible their specific knowledge communities.
information network (HIN) analysis. Given its long tradition of In the next section, we present the theoretical foundations of
investigating the politics of knowledge and meaning-making, our work, informed by a sociology of knowledge perspective on
social science discourse research can help to avoid the common discourse. This is followed, in section 3, by a description of our
pitfalls of studying insulated information elements without methodology. In section 4, we present an exemplary online
reflecting the relevant “social relations of knowledge and discourse analysis of a publicly available, large-scale dataset of
Covid19 Tweets. We chose the pandemic as context, as we
knowing” [1, p. 18]. With its toolboxes for the inquiry of the
expect the respective dataset to indeed represent a complex
social construction of reality, the discourse analytical approach
network of discursive formations and structures of knowledge
can help to go beyond facts, especially when studying online production.
communication. These toolboxes, however, need renewal and
KnOD'21 Workshop - April 14, 2021
Copyright © 2021 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
KnOD’21, April, 2021, Ljubljana, Slovenia A. Brand et al.
information flows by its users. While user influence on the
platform follows a power-law distribution, all users are free to
distribute, share and comment on information with their own
2 Theoretical Foundations followers, effectively providing the tools to collectively shape
Knowledge is an ambiguous concept. Even in empirical social information environments in a network-based manner [21]. By
sciences, it is frequently understood as a measurable resource looking at Twitter, we can make visible the processes which
of individual people. This conception is especially prevalent in filter and curate the information environments of its users
the fields of political studies, including political psychology and without neglecting the role of these very users and their
political sociology, wherein - somewhat surprisingly - networks.
knowledge is mostly conceived as an individual instead of a
collective resource [7, 8]. Theoretically and methodologically,
this goes along with a widely shared pre-occupation with the 3 Methodology
micro-level foundations of social action in political studies at Methodologically, we use a two-stage grouping procedure.
the expense of the relational dimension [9, 10] essential for First, we obtain a mesoscopic representation of the network.
knowledge production. In contrast to such prevalent Following Bar-Hen et al [22], such a representation of the
conceptions, we root ourselves in a sociology of knowledge network is obtained by grouping together nodes of the same
tradition [11, 12, 13, 14]. Moreover, the accompanying entity and the same cluster and displaying them as blocks. This
methodological re-orientation towards a social science representation is very similar to a general block model with the
research tradition of discourse analysis helps to avoid notable difference of additional separation by node type. The
individualist misconceptions of knowledge and provides choice to use a block model method in the first step was made
research methods that allow to go beyond facts in the empirical with regard to the good performance of such models for large
inquiry of the social construction of knowledge. This seems networks. Additionally, it allowed us to draw on applied
particularly helpful these days, as so-called disinformation is research on combined clustering of multiple types of entities,
increasingly gathering academic interest and the scholars such as documents and text in the case of Gerlach et al [23]. In
involved are running the risk of neglecting the social dimension the second step, a simpler clustering procedure can be carried
in the production of knowledge [15]. out, which takes the edge weights into account. In the following
chapters we refer to the clustered mesoscopic view of the
Delving into processes of collective meaning-making by
network as the macroscopic view and to its clusters as
applying discourse analytical methods is essential, as
macroclusters.
“information by itself usually has no value: it is a raw material
that gains value if further processed in specific ways and if
meaning and a certain quality are attached to it”[16, p. 15]. 4 Exemplary Analysis
Thus, knowledge cannot belong to the features of an individual
(a user of digital media in our case) but is produced, processed For our analysis we used the TweetsCOV19 dataset [24]. We
in and obtained from discourses. Information or facts are chose the pandemic as context not just due to its current
‘consumed’ by users only through these collectively built filters relevance, but because we expect the respective dataset to
of perception. While emanating from a Foucauldian, post- indeed represent a complex network of discursive formations
structuralist tradition, discourse analysis does not necessarily and knowledge communities. This would include, among
mean to neglect the crucial relevance of agency. It is our social- others, various special discourses of scientific experts,
constructivist conception that makes us attribute a hub-like governmental communicative discourses, as well as general
role to actors (here: users of online media) in the basic design public discourse co-constituted by mainstream and social
of our complex networks instead (see analysis section below). media. TweetsCOV19 is an annotated publicly available Twitter
As discourses “are performed through social actors’ (often corpus of more than 8 million tweets on Covid19, including
competing or conflictual) discursive practices” [17, p. 3] it is data from October 2019 - April 2020. For our analysis, we used
actors that performatively produce the linkages that we can a restricted version starting with the first public appearance of
map in a network, be it links to other entities that are a Covid19 case in the general media on 12/31/2019,
identifiable in online discourses such as URLs, hashtags, named preventing false positive matches. After this we build our
entities, or other users. Taking such entities not only as sample consisting of 6.317.324 tweets. A timeline of the
linkages in communicative networks but as constitutive number of tweets can be found in Appendix 1.
elements of issue publics or even communities of discourse and We proceeded as follows: In the first step, we constructed a
knowledge, we can rely on theoretical and analytical
poly-partite network from the tweets with the username,
assumptions developed in the field of digital communication
studies [18, 19, 20]. mentions, hashtags, URLs and named entities as node types and
edges of one type which symbolize references (e.g., User X uses
These relational patterns can be made visible by studying Hashtag Y in a tweet). Named entities were extracted using
digital trace data at a large scale. The social media platform scores from the Fast Entity Linker Core library and URLs were
Twitter provides a particularly well-suited test case for our
expanded when necessary [24]. Furthermore, we removed stop
methodology. Twitter, with its characteristics of both a social
words from user mentions, hashtags and named entities. This
network and an information network, makes visible the
formation of knowledge communities through the curation of led to a poly-partite network with the following properties:
Using heterogenous information networks for integrative
KnOD’21, April, 2021, Ljubljana, Slovenia
discourse mapping
are rather strongly separated from each other, while a fifth
Table 1: Basic metrics of the constructed poly-partite (grey) is more torn apart. However, we also observe some
network outliers. For example, three blocks of the third macrocluster
Metric Value are located relatively apart from the rest of their cluster.
Total sum of nodes 708.352
Sum of unique usernames 130.997
Sum of unique user mentions 176.991
Sum of unique hashtags 158.438
Sum of unique URLs 145.041
Sum of unique named entities 96.885
Total sum of edges 111.399.912
In the next step, an agglomerative collapsing algorithm [25, 26] Figure 1: Full macroscopic view of the network with a
was used to block the nodes in the network. Following our stress-based layout. The background colour symbolizes
agent-centric theoretical assumptions that knowledge is the assignment of the nodes to the respective macrocluster
produced by a community of users (see above), interblock (Dirichlet tessellation). The node size represents the
connections consist of user-user, user-hashtag, user-URL, and number of nodes in the blocks of the poly-partite network.
user-named entity relations. Due to the large amount of edges The visibility of the edges symbolizes the number of
an agglomerative heuristic was applied, which iteratively tries connections between blocks. Blocks with less than 0.1% of
to find a better configuration of blocks by progressively all out-going edges were cut from the representation to
merging blocks together [25]. The final model selected via the support the visualization.
lowest entropy criteria consists of 16 named entity blocks, 28
hashtag blocks, 19 user mention blocks, 23 URL blocks and 14 The annotated version of the network (Figure 2) enables a
username blocks. An overview of the number of nodes in each more qualitative look at the different areas of discourse.
block can be found in Appendix 2. In the next step, the Consistent with Figure 1, it is observable that macrocluster one
macroclusters were computed via a simple greedy clustering is a mixed compilation with no clear identity. The second
algorithm, clustering the blocks in the mesoscopic network. In cluster, “Organizational Aspect and Early Response“, contains
the last step a qualitative coding of the blocks and clusters was blocks associated with aspects like the role of organizations in
performed. To ensure the interpretability of the results, we the pandemic and early response actions like the proposed
calculated the PageRank of each node in the original poly- usage of Hydroxychloroquine for the treatment of Covid19
partite network and considered the top 10 nodes per block for patients. The third cluster, “Technology and Daily Life“, takes a
the coding, similar to the evaluation of structural topic models deeper dive into media, culture, and technological aspects.
and in line with Twitter’s power-law distribution. For the There is a certain proximity to macrocluster two, which is also
coding of the usernames, we further included the account notable in Figure 1. The aforementioned three outliers are
description into the coding step. URLs were coded via clues in more related to topics like weapons and US politics. The fourth
the URL title. Generally, we used simple heuristics for content cluster, “Culture and Safety“, deals more with aspects like
coding. For example, clusters containing actors from the fields mental health and media, while the fifth cluster, “Uncertainty”,
of music, art and film were coded as "Cultural", while URLs focusses on the uncertainties of living through the pandemic.
coded with "Protection from Covid19" contain reports on Following this differentiation, we can see that the chosen
different levels of protection in relation to aspects like representation suggests a description of the Covid19 corpus
ethnicity. along the lines of organizational aspects, reaction compulsion,
cultural and technical adaptations, media use and general
uncertainty. These aspects do not appear in isolation, but
5 Results within the framework of a complex web of different emphases
Our results indicate a heterogenous discursive space. For the and affiliations.
evaluation of the results, we present two novel visualizations:
A full macroscopic view of the poly-partite network and a
qualitatively annotated visualization of each macrocluster.
The macroscopic representation allows to visualize the general
structure of the network in a representation similar to
discourse maps commonly used in social science discourse
research. As can be seen in the Figure 1, four of the five groups
KnOD’21, April, 2021, Ljubljana, Slovenia A. Brand et al.
the explorative analysis of the Covid19 pandemic on Twitter,
five macroclusters with differing users, URLs, hashtags, named
entities and mentions became visible. As such, we can identify
these clusters as knowledge communities, collectively shaping
heterogenous information environments through their intra-
and intercluster relations. In order to exhaust the possibilities
of this approach, future analyses should consider utilizing even
more diverse types of data to compute as clusters. The
framework is highly flexible and able to incorporate multiple
data sources and types of nodes. This flexibility can stretch to
different types of data, such as textual or visual analyses, and
even heterogenous data sources, such as different platforms.
Furthermore, HINs allow for the specification of different edge
types for an even more sophisticated model. This allows
researchers to tailor their analysis around specific subjects
without compromising neither theoretical foundations nor
scalability. However, the selection of nodes should be theory-
driven in order to avoid arbitrariness and remain economical
with regard to computational resources. As such, our next steps
to improve the information richness of the macroclusters
would be the implementation of quantitative text analysis into
the model, giving a more in-depth look into the knowledge
communities surrounding the Covid19 pandemic on Twitter
beyond facts.
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Appendix 2: Number of Nodes in each Block