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Facilitating on-line opinion dynamics by mining expressions of causation. The case of climate change debates on The Guardian

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News website comment sections are spaces where potentially conflicting opinions and beliefs are voiced. Addressing questions of how to study such cultural and societal conflicts through technological means, the present article critically examines possibilities and limitations of machine-guided exploration and potential facilitation of on-line opinion dynamics. These investigations are guided by a discussion of an experimental observatory for mining and analyzing opinions from climate change-related user comments on news articles from the TheGuardian.com. This observatory combines causal mapping methods with computational text analysis in order to mine beliefs and visualize opinion landscapes based on expressions of causation. By (1) introducing digital methods and open infrastructures for data exploration and analysis and (2) engaging in debates about the implications of such methods and infrastructures, notably in terms of the leap from opinion observation to debate facilitation, the article aims to make a practical and theoretical contribution to the study of opinion dynamics and conflict in new media environments.
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Facilitating on-line opinion dynamics by
mining expressions of causation. The
case of climate change debates on The
Guardian
Tom Willaert1, Sven Banisch2, Paul Van Eecke1and Katrien Beuls1
Abstract
News website comment sections are spaces where potentially conflicting opinions and beliefs are voiced. Addressing
questions of how to study such cultural and societal conflicts through technological means, the present article
critically examines possibilities and limitations of machine-guided exploration and potential facilitation of on-line opinion
dynamics. These investigations are guided by a discussion of an experimental observatory for mining and analyzing
opinions from climate change-related user comments on news articles from the TheGuardian.com. This observatory
combines causal mapping methods with computational text analysis in order to mine beliefs and visualize opinion
landscapes based on expressions of causation. By (1) introducing digital methods and open infrastructures for data
exploration and analysis and (2) engaging in debates about the implications of such methods and infrastructures,
notably in terms of the leap from opinion observation to debate facilitation, the article aims to make a practical and
theoretical contribution to the study of opinion dynamics and conflict in new media environments.
Keywords
media, data mining, opinion dynamics, beliefs, conflict, debate, causal mapping, climate change, Guardian, digital
methods
Introduction
Background
Over the past two decades, the rise of social media and the
digitization of news and discussion platforms have radically
transformed how individuals and groups create, process
and share news and information. As Alan Rusbridger,
former-editor-in-chief of the newspaper The Guardian has
it, these technologically-driven shifts in the ways people
communicate, organize themselves and express their beliefs
and opinions, have
empower[ed] those that were never heard,
creating a a new form of politics and turning
traditional news corporations inside out. It is
impossible to think of Donald Trump; of Brexit;
of Bernie Sanders; of Podemos; of the growth
of the far right in Europe; of the spasms of
hope and violent despair in the Middle East
and North Africa without thinking also of the
total inversion of how news is created, shared
and distributed. Much of it is liberating and
and inspiring. Some of it is ugly and dark. And
something - the centuries-old craft of journalism
- is in danger of being lost (Rusbridger 2018, xx-
xxi).
Rusbridger’s observation that the present media-ecology
puts traditional notions of politics, journalism, trust and
truth at stake is a widely shared one (see for instance
Lichfield 2018;Singer and Brooking 2018;Sunstein 2018).
As such, it has sparked interdisciplinary investigations,
diagnoses and ideas for remedies across the economical,
socio-political, and technological spectrum, challenging our
existing assumptions and epistemologies (see Floridi 2013,
2014). Among these lines of inquiry, particular strands
of research from the computational social sciences are
addressing pressing questions of how emerging technologies
and digital methods might be operationalized to regain a grip
on the dynamics that govern the flow of on-line news and
its associated multitudes of voices, opinions and conflicts.
Could the information circulating on on-line (social) news
platforms for instance be mined to better understand and
analyze the problems facing our contemporary society?
Might such data mining and analysis help us to monitor
the growing number of social conflicts and crises due to
cultural differences and diverging world-views? And finally,
would such an approach potentially facilitate early detection
of conflicts and even ways to resolve them before they turn
violent?
Answering these questions requires further advances
in the study of cultural conflict based on digital media
data. This includes the development of fine-grained
representations of cultural conflict based on theoretically-
informed text analysis, the integration of game-theoretical
approaches to models of polarization and alignment, as
well as the construction of accessible tools and media-
monitoring observatories: platforms that foster insight into
1VUB Articial Intelligence Lab, Vrije Universiteit Brussel, Pleinlaan 2,
1050 Brussels, Belgium
2Max Planck Institute for Mathematics in the Sciences, Inselstrae 22,
Leipzig, Germany
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the complexities of social behaviour and opinion dynamics
through automated computational analyses of (social) media
data. Through an interdisciplinary approach, the present
article aims to make both a practical and theoretical
contribution to these aspects of the study of opinion
dynamics and conflict in new media environments.
Objective
The objective of the present article is to critically examine
possibilities and limitations of machine-guided exploration
and potential facilitation of on-line opinion dynamics on
the basis of an experimental data analytics pipeline or
observatory for mining and analyzing climate change-related
user comments from the news website of The Guardian
(TheGuardian.com). Combining insights from the social
and political sciences with computational methods for the
linguistic analysis of texts, this observatory provides a
series of spatial (network) representations of the opinion
landscapes on climate change on the basis of causation
frames expressed in news website comments. This allows for
the exploration of opinion spaces at different levels of detail
and aggregation.
Technical and theoretical questions related to the proposed
method and infrastructure for the exploration and facilitation
of debates will be discussed in three sections. The first
section concerns notions of how to define what constitutes
a belief or opinion and how these can be mined from texts.
To this end, an approach based on the automated extraction
of semantic frames expressing causation is proposed. The
observatory thus builds on the theoretical premise that
expressions of causation such as ‘global warming causes
rises in sea levels’ can be revelatory for a person or group’s
underlying belief systems. Through a further technical
description of the observatory’s data-analytical components,
section two of the paper deals with matters of spatially
modelling the output of the semantic frame extractor and
how this might be achieved without sacrificing nuances of
meaning. The final section of the paper, then, discusses
how insights gained from technologically observing opinion
dynamics can inform conceptual modelling efforts and
approaches to on-line opinion facilitation. As such, the paper
brings into view and critically evaluates the fundamental
conceptual leap from machine-guided observation to debate
facilitation and intervention.
Through the case examples from The Guardian’s website
and the theoretical discussions explored in these sections, the
paper intends to make a twofold contribution to the fields of
media studies, opinion dynamics and computational social
science. Firstly, the paper introduces and chains together a
number of data analytics components for social media mon-
itoring (and facilitation) that were developed in the context
of the <project name anonymized for review>infrastruc-
ture project. The <project name anonymized for review>
infrastructure makes the components discussed in this paper
available as open web services in order to foster reproducibil-
ity and further experimentation and development <infras-
tructure reference URL anonymized for review>. Secondly,
and supplementing these technological and methodologi-
cal gains, the paper addresses a number of theoretical,
epistemological and ethical questions that are raised by
experimental approaches to opinion exploration and facili-
tation. This notably includes methodological questions on
the preservation of meaning through text and data mining,
as well as the role of human interpretation, responsibility
and incentivisation in observing and potentially facilitating
opinion dynamics.
Data: the communicative setting of
TheGuardian.com
In order to study on-line opinion dynamics and build the
corresponding climate change opinion observatory discussed
in this paper, a corpus of climate-change related news articles
and news website comments was analyzed. Concretely,
articles from the climate change subsection from the news
website of The Guardian dated from 2009 up to April
2019 were processed, along with up to 200 comments
and associated metadata for articles where commenting
was enabled at the time of publication. The choice for
studying opinion dynamics using data from The Guardian
is motivated by this news website’s prominent position in the
media landscape as well as its communicative setting, which
is geared towards user engagement. Through this interaction
with readers, the news platform embodies many of the recent
shifts that characterize our present-day media ecology.
TheGuardian.com is generally acknowledged to be one of
the UK’s leading online newspapers, with 8,2 million unique
visitors per month as of May 2013 (Reid 2018). The website
consists of a core news site, as well as a range of subsections
that allow for further classification and navigation of articles.
Articles related to climate change can for instance be
accessed by navigating through the ‘News’ section, over
the subsection ‘environment’, to the subsubsection ‘climate
change’ (Guardian 2019b). All articles on the website can
be read free of charge, as The Guardian relies on a business
model that combines revenues from advertising, voluntary
donations and paid subscriptions.
Apart from offering high-quality, independent journalism
on a range of topics, a distinguishing characteristic of
The Guardian is its penchant for reader involvement and
engagement. Adopting to the changing media landscape and
appropriating business models that fit the transition from
print to on-line news media, the Guardian has transformed
itself into a platform that enables forms of citizen journalism,
blogging, and welcomes readers comments on news articles
(for this transition, see for instance Rusbridger 2018, chap.
11). In order for a reader to comment on articles, it is required
that a user account is made, which provides a user with
a unique user name and a user profile page with a stable
URL. According to the website’s help pages, providing
users with an identity that is consistently recognized by
the community fosters proper on-line community behaviour
(Guardian 2010). Registered users can post comments on
content that is open to commenting, and these comments
are moderated by a dedicated moderation team according
to The Guardian’s community standards and participation
guidelines (Guardian 2009). In support of digital methods
and innovative approaches to journalism and data mining,
The Guardian has launched an open API (application
programming interface) through which developers can
access different types of content (Guardian 2018). It should
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be noted that at the moment of writing this article, readers’
comments are not accessible through this API. For the
scientific and educational purposes of this paper, comments
were thus consulted using a dedicated scraper.
Taking into account this community and technologically-
driven orientation, the communicative setting of The
Guardian from which opinions are to be mined and the
underlying belief system revealed, is defined by articles,
participating commenters and comment spheres (that is, the
actual comments aggregated by user, individual article or
collection of articles) (see Figure 1).
A controversy, most
often, involves a series
of issues that are logic
ally and cognitively
linked.
A proper spatial
representation of such
complexity should
therefore take into
account the
multi-dimensionality
and interconnectedness
of the points at stake.
We propose to model
this by a network the
nodes of which are key
issues in a debate and
the links weights for
relations in between
those.
Using survey data or
argument data extracted
from text with precision
language processing
techniques to inform the
underlying opinion
structure will open up
completely new ways of
model validation. The
argument exchange
mechanism studied here
Online Articles on
Climate Change
Comment
Spheres
Participating
Commentators
discussions and
debates on its political
and institutional
implications ...
the very idea of an
opinion facititator
raises serious
doubts
is becoming more
and more
germane to
understanding
opinion exchange
the very idea of
an opinion facititator
raises serious doubts
experimental
evidence on group
polarization
and choice shifts
through group
groups inclined to a certain
attitudinal direction rely on a limited
argument pool which is biased into
the respective direction as there is a
disproportionate number of supporting
claims
the dynamics of
political opinions is a
complicated issue
links different
(partially
overlapping)
sets of facts
different
theoretical
pieces must be
brought
together
the very idea of
an opinion facititator
raises serious doubts
incentives and
rewards of opinion
expression in
different social
groups inclined to a certain
attitudinal direction rely on a limited
argument pool which is biased into
the respective direction as there is a
disproportionate number of supporting
claims
leads to the formation
of complex landscapes
of political preferences
distinguish
arguments and
beliefs about
facts from a
political issue
A cognitive
structure of
evaluative
associations
the interplay of a
manifold of
psychological and
social processes
the very idea of
an opinion facititator
raises serious doubts
groups inclined to a certain
attitudinal direction rely on a limited
argument pool which is biased into
the respective direction as there is a
disproportionate number of supporting
claims
Figure 1. Communicative setting of many online newspaper
sites. The newspaper publishes articles on different topics and
users can comment on these articles and previous comments.
In this setting, articles (and previous comments on those
articles) can be commented on by participating commenters,
each of which bring to the debate his or her own opinions or
belief system. What this belief system might consists of can
be inferred on a number of levels, with varying degrees of
precision. On the most general level, a generic description
of the profile of the average reader of The Guardian can
be informative. Such profiles have been compiled by market
researchers with the purpose of informing advertisers about
the demographic that might be reached through this news
website (and other products carrying The Guardian’s brand).
As of the writing of this article, the audience The Guardian
is presented to advertisers as a ‘progressive’ audience:
Living in a world of unprecedented societal
change, with the public narratives around
politics, gender, body image, sexuality and
diet all being challenged. The Guardian is
committed to reflecting the progressive agenda,
and reaching the crowd that uphold those
values. Its helpful that we reach over half of
progressives in the UK (Guardian 2019a).
A second, equally high-level indicator of the beliefs that
might be present on the platform, are the links through which
articles on climate change can be accessed. An article on
climate change might for instance be consulted through the
environment section of the news website, but also through
the business section. Assuming that business interests might
potentially be at odds with environmental concerns, it could
be hypothesized that the particular comment sphere for that
article consists of at least two potentially clashing frames of
mind or belief systems.
However, as will be expanded upon further in this
article, truly capturing opinion dynamics requires a more
systemic and fine-grained approach. The present article
therefore proposes a method for harvesting opinions from
the actual comment texts. The presupposition is thereby that
comment spheres are marked by a diversity of potentially
related opinions and beliefs. Opinions might for instance
be connected through the reply structure that marks the
comment section of an article, but this connection might
also manifest itself on a semantic level (that is, the level of
meaning or the actual contents of the comments). To capture
this multidimensional, interconnected nature of the comment
spheres, it is proposed to represent comment spheres as
networks, where the nodes represent opinions and beliefs,
and edges the relationships between these beliefs (see the
spatial representation of beliefs infra). The use of precision
language tools to extract such beliefs and their mutual
relationships, as will be explored in the following sections,
can open up new pathways of model validation and creation.
Mining opinions and beliefs from texts
In traditional experimental settings, survey techniques
and associated statistical models provide researchers with
established methods to gauge and analyze the opinions of
a population. When studying opinion landscapes through
on-line social media, however, harvesting beliefs from big
textual data such as news website comments and developing
or appropriating models for their analysis is a non-trivial
task (for an overview of methodological challenges facing
computational social science and digital methods, see for
instance Watts 2013;Rogers 2013,2019).
In the present context, two challenges related to data-
gathering and text mining need to be addressed: (1) defining
what constitutes an expression of an opinion or belief, and
(2) associating this definition with a pattern that might
be extracted from texts. Recent scholarship in the fields
of natural language processing (NLP) and argumentation
mining has yielded a range of instruments and methods
for the (automatic) identification of argumentative claims
in texts (see for instance Farzindar et al. 2017;Stede et al.
2018). Adding to these instruments and methods, the present
article proposes an approach in which belief systems or
opinions on climate change are accessed through expressions
of causation.
Causal mapping methods and the climate
change debate
The climate change debate is often characterized by
expressions of causation, that is, expressions linking a certain
cause with a certain effect. Cultural or societal clashes
on climate change might for instance concern diverging
assessments of whether global warming is man-made or
not (for a sample of arguments in favour of or against
anthropogenic global warming, see ProCon 2019). Based
on such examples, it can be stated that expressions of
causation are closely associated with opinions or beliefs,
and that as such, these expressions can be considered
a valuable indicator for the range and diversity of the
opinions and beliefs that constitute the climate change
debate. The observatory under discussion therefore focuses
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on the extraction and analysis of linguistic patterns called
causation frames. As will be further demonstrated in this
section, the benefit of this causation-based approach is that
it offers a systemic approach to opinion dynamics that
comprises different layers of meaning, notably the cognitive
or social meaningfulness of patterns on account of their
being expressions of causation, as well as further lexical and
semantic information that might be used for analysis and
comparison.
The study of expressions of causation as a method
for accessing and assessing belief systems and opinions
has been formalized and streamlined since the 1970s.
Pioneered by political scientist Robert Axelrod and others,
this causal mapping method (also referred to as ‘cognitive
mapping’) was introduced as a means of reconstructing
and evaluating administrative and political decision-making
processes, based on the principle that
the notion of causation is vital to the process
of evaluating alternatives. Regardless of philo-
sophical difficulties involved in the meaning of
causation, people do evaluate complex policy
alternatives in terms of the consequences a par-
ticular choice would cause, and ultimately of
what the sum of these effects would be. Indeed,
such causal analysis is built into our language,
and it would be very difficult for us to think
completely in other terms, even if we tried
(Axelrod 2016, 5).
Axelrod’s causal mapping method comprises a set of
conventions to graphically represent networks of causes and
effects (the nodes in a network) as well as the qualitative
aspects of this relation (the networks directed edges, notably
assertions of whether the causal linkage is positive or
negative). These causes and effects are to be extracted from
relevant sources by means of a series of heuristics and
an encoding scheme (it should be noted that for this task
Axelrod had human readers in mind). The graphs resulting
from these efforts provide a structural overview of the
relations among causal assertions (and thus beliefs):
The basic elements of the proposed system
are quite simple. The concepts a person uses
are represented as points, and the causal links
between these concepts are represented as
arrows between these points. This gives a
pictorial representation of the causal assertions
of a person as a graph of points and arrows.
This kind of representation of assertions as a
graph will be called a cognitive map. The policy
alternatives, all of the various causes and effects,
the goals, and the ultimate utility of the decision
maker can all be thought of as concept variables,
and represented as points in the cognitive map.
The real power of this approach appears when
a cognitive map is pictured in graph form; it
is then relatively easy to see how each of the
concepts and causal relationships relate to each
other, and to see the overall structure of the
whole set of portrayed assertions (Axelrod 2016,
5).
In order to construct these cognitive maps based on textual
information, Margaret Tucker Wrightson provides a set of
reading and coding rules for extracting cause concepts,
linkages (relations) and effect concepts from expressions in
the English language. The assertion ‘Our present topic is
the militarism of Germany, which is maintaining a state of
tension in the Baltic Area’ might for instance be encoded
as follows: ‘the militarism of Germany’ (cause concept), /+/
(a positive relationship), ‘maintaining a state of tension in
the Baltic area’ (effect concept) (Tucker Wrightson 2016,
296-297). Emphasizing the role of human interpretation, it
is acknowledged that no strict set of rules can capture the
entire spectrum of causal assertions:
The fact that the English language is as varied as
those who use it makes the coder’s task complex
and difficult. No set of rules will completely
solve the problems he or she might encounter.
These rules, however, provide the coder with
guidelines which, if conscientiously followed,
will result in outcomes meeting social scientific
standards of comparative validity and reliability
(Tucker Wrightson 2016, 332).
To facilitate the task of encoders, the causal mapping
method has gone through various iterations since its original
inception, all the while preserving its original premises.
Recent software packages have for instance been devised
to support the data encoding and drawing process (see for
instance Laukkanen and Wang 2015). As such, causal or
cognitive mapping has become an established opinion and
decision mining method within political science, business
and management, and other domains. It has notably proven
to be a valuable method for the study of recent societal and
cultural conflicts. Thomas Homer-Dixon et al. for instance
rely on cognitive-affective maps created from survey data
to analyze interpretations of the housing crisis in Germany,
Israeli attitudes toward the Western Wall, and moderate
versus skeptical positions on climate change (Homer-Dixon
et al. 2014). Similarly, Duncan Shaw et al. venture to answer
the question of ‘Why did Brexit happen?’ by building causal
maps of nine televised debates that were broadcast during the
four weeks leading up to the Brexit referendum (Shaw et al.
2017).
In order to appropriate the method of causal mapping to
the study of on-line opinion dynamics, it needs to expanded
from applications at the scale of human readers and relatively
small corpora of archival documents and survey answers,
to the realm of ‘big’ textual data and larger quantities of
information. This attuning of cognitive mapping methods
to the large-scale processing of texts required for media
monitoring necessarily involves a degree of automation, as
will be explored in the next section.
Automated causation tracking with the
Penelope semantic frame extractor
As outlined in the previous section, causal mapping is
based on the extraction of so-called cause concepts, (causal)
relations, and effect concepts from texts. The complexity
of each of these these concepts can range from the
relatively simple (as illustrated by the easily-identifiable
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cause and effect relation in the example of ‘German
militarism’ cited earlier), to more complex assertions such
as ‘The development of international cooperation in all
fields across the ideological frontiers will gradually remove
the hostility and fear that poison international relations’,
which contains two effect concepts (viz. ‘the hostility that
poisons international relations’ and ‘the fear that poisons
international relations’). As such, this statement would have
to be encoded as a double relationship (Tucker Wrightson
2016, 297-298).
The coding guidelines in Tucker Wrightson (2016) further
reflect that extracting cause and effect concepts from texts is
an operation that works on both the syntactical and semantic
levels of assertions. This can be illustrated by means of the
guidelines for analyzing the aforementioned causal assertion
on German militarism:
1. The first step is the realization of the
relationship. Does a subject affect an object?
2. Having recognized that it does, the isolation
of the cause and effects concepts is the second
step. As the sentence structure indicates, ”the
militarism of Germany” is the causal concept,
because it is the initiator of the action, while
the direct object clause, ”a state of tension
in the Baltic area,” constitutes that which
is somehow influenced, the effect concept
(Tucker Wrightson 2016, 296).
In the field of computational linguistics, from which the
present paper borrows part of its methods, this procedure for
extracting information related to causal assertions from texts
can be considered an instance of an operation called semantic
frame extraction (for the concept of semantic frames, see
Fillmore 1982). A semantic frame captures a coherent part
of the meaning of a sentence in a structured way. As
documented in the FrameNet project (Baker et al. 1998), the
CAUS ATIO N frame is defined as follows:
A Cause causes an Effect. Alternatively, an
Actor, a participant of a (implicit) Cause, may
stand in for the Cause. The entity Affected by
the Causation may stand in for the overall Effect
situation or event (Framenet 2001).
In a linguistic utterance such as a statement in a news
website comment, the CAUSATION frame can be evoked by a
series of lexical units, such as ‘cause’, ‘bring on’, etc. In the
example ‘If such a small earthquake CAUSES problems, just
imagine a big one!’, the CAUSATION frame is triggered by
the verb ‘causes’, which therefore is called the frame evoking
element. The CAUSE slot is filled by ‘a small earthquake’, the
EFFE CT slot by ‘problems’ (Framenet 2001).
In order to automatically mine cause and effects concepts
from the corpus of comments on The Guardian, the present
paper uses the Penelope semantic frame extractor: a tool
that exploits the fact that semantic frames can be expressed
as form-meaning mappings called constructions. Notably,
frames were extracted from Guardian comments by focusing
on the following lexical units (verbs, prepositions and
conjunctions), listed in FrameNet as frame evoking elements
of the CAUSATION frame: CAUSE.V, D UE T O.P RE P,
BEC AUS E.C, BECAUSE OF.PR EP, GIV E RI SE T O.V, LEAD
TO.Vor RESULT I N.V.
As illustrated by the following examples, the strings
output by the semantic frame extractor adhere closely to
the original utterance, preserving all of the the comments’
causation frames real-world noisiness:
{
"causalRelations": [
{
"utterance": "Has anyone
totted up the extra
pollution on London
streets emanating from
traffic jams caused by
Extinction Rebellion
?",
"cause": "extinction
rebellion",
"effect": "traffic jams"
}
]
}
The output of the semantic frame extractor as such is
used as the input for the ensuing pipeline components in the
climate change opinion observatory. The aim of a further
analysis of these frames is to find patterns in the beliefs
and opinions they express. As will be discussed in the
following section, which focuses on applications and cases,
maintaining semantic nuances in this further analytic process
foregrounds the role of models and aggregation levels.
Analyses and applications
Based on the presupposition that relations between causation
frames reveal beliefs, the output of the semantic frame
extractor creates various opportunities for exploring opinion
landscapes and empirically validating conceptual models for
opinion dynamics.
In general, any alignment of conceptual models and real-
world data is an exercise in compromising, as the idealized,
abstract nature of models is likely to be at odds with the
messiness of the actual data. Finding such a compromise
might for instance involve a reduction of the simplicity or
elegance of the model, or, on the other hand, an increased
aggregation (and thus reduced granularity) of the data.
Addressing this challenge, the current section reflects
on questions of data modelling, aggregation and meaning
by exploring, through case examples, different spatial rep-
resentations of opinion landscapes mined from the The-
Guardian.com’s comment sphere. These spatial renditions
will be understood as network visualizations in which nodes
represent argumentative statements (beliefs) and edges the
degree of similarity between these statements. On the most
general level, then, such a representation can consists of
an overview of all the causes expressed in the corpus of
climate change-related Guardian comments. This type of
visualization provides a birds-eye view of the entire opinion
landscape as mined from the comment texts. In turn, such
a general overview might elicit more fine-grained, micro-
level investigations, in which a particular cause is singled
out and its more specific associated effects are mapped.
These macro and micro level overviews come with their own
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proper potential for theory building and evaluation, as well as
distinct requirements for the depth or detail of meaning that
needs to be represented. To get the most general sense of an
opinion landscape one might for instance be more tolerant of
abstract renditions of beliefs (e.g. by reducing statements to
their most frequently used terms), but for more fine-grained
analysis one requires more context and nuance (e.g. adhering
as closely as possible to the original comment).
Aggregation
As follows from the above, one of the most fundamental
questions when building automated tools to observe opinion
dynamics that potentially aim at advising means of debate
facilitation concerns the level of meaning aggregation.
A clear argumentative or causal association between, for
instance, climate change and catastrophic events such as
floods or hurricanes may become detectable by automatic
causal frame tracking at the scale of large collections of
articles where this association might appear statistically
more often, but detection comes with great challenges when
the aim is to classify certain sets of only a few statements
in more free expression environments such as comment
spheres.
In other words, the problem of meaning aggregation
is closely related to issues of scale and aggregation over
utterances. The more fine-grained the semantic resolution
is, that is, the more specific the cause or effect is that one
is interested in, the less probable it is to observe the same
statement twice. Moreover, with every independent variable
(such as time, different commenters or user groups, etc.),
less data on which fine-grained opinion statements are to be
detected is available. In the present case of parsed comments
from TheGuardian.com, providing insights into the belief
system of individual commenters, even if all their statements
are aggregated over time, relies on a relatively small set
of argumentative statements. This relative sparseness is in
part due to the fact that the scope of the semantic frame
extractor is confined to the frame evoking elements listed
earlier, thus omitting more implicit assertions of causation
(i.e. expressions of causation that can only be derived from
context and from reading between the lines).
Similarly, as will be explored in the ensuing paragraphs,
matters of scale and aggregation determine the types
of further linguistic analyses that can be performed on
the output of the frame extractor. Within the field of
computational linguistics, various techniques have been
developed to represent the meaning of words as vectors
that capture the contexts in which these words are typically
used. Such analyses might reveal patterns of statistical
significance, but it is also likely that in creating novel,
numerical representations of the original utterances, the
semantic structure of argumentatively linked beliefs is lost.
In sum, developing opinion observatories and (potential)
debate facilitators entails finding a trade-off, or, in fact, a
middle way between macro- and micro-level analyses. On
the one hand, one needs to leverage automated analysis
methods at the scale of larger collections to maximum
advantage. But one also needs to integrate opportunities
to interactively zoom into specific aspects of interest and
provide more fine-grained information at these levels down
to the actual statements. This interplay between macro- and
micro-level analyses is explored in the case studies below.
Spatial renditions of TheGuardian.com’s
opinion landscape
The main purpose of the observatory under discussion is to
provide insight into the belief structures that characterize the
opinion landscape on climate change. For reasons outlined
above, this raises questions of how to represent opinions and,
correspondingly, determining which representation is most
suited as the atomic unit of comparison between opinions.
In general terms, the desired outcome of further processing
of the output of the semantic frame extractor is a network
representation in which similar cause or effect strings are
displayed in close proximity to one another. A high-level
description of the pipeline under discussion thus goes as
follows. In a first step, it can be decided whether one wants to
map cause statements or effect statements. Next, the selected
statements are grouped per commenter (i.e. a list is made
of all cause statements or effect statements per commenter).
These statements are filtered in order to retain only nouns,
adjectives and verbs (thereby also omitting frequently
occurring verbs such as to be). The remaining words are
then lemmatized, that is, reduced to their dictionary forms.
This output is finally translated into a network representation,
whereby nodes represent (aggregated) statements, and edges
express the semantic relatedness between statements (based
on a set overlap whereby the number of shared lemmata are
counted).
As illustrated by two spatial renditions that were created
using this approach and visualized using the network
analysis tool Gephi (Bastian et al. 2009), the labels assigned
to these nodes (lemmata, full statements, or other) can be
appropriated to the scope of the analysis.
A macro-level overview: causes addressed in the climate
change debate Suppose one wants to get a first idea about
the scope and diversity of an opinion landscape, without
any preconceived notions of this landscape’s structure or
composition. One way of doing this would be to map all
of the causes that are mentioned in comments related to
articles on climate change, that is, creating an overview of
all the causes that have been retrieved by the frame extractor
in a single representation. Such a representation would not
immediately provide the granularity to state what the beliefs
or opinions in the debates actually are, but rather, it might
inspire a sense of what those opinions might be about,
thus pointing towards potentially interesting phenomena that
might warrant closer examination.
Figure 2, a high-level overview of the opinion landscape,
reveals a number of areas to which opinions and beliefs
might pertain. The top-left clusters in the diagram for
instance reveal opinions about the role of people and
countries, whereas on the right-hand side, we find a
complementary cluster that might point to beliefs concerning
the influence of high or increased CO2-emissions. In
between, there is a cluster on power and energy sources,
reflecting the energy debate’s association to both issues
of human responsibility and CO2 emissions. As such, the
overview can already inspire, potentially at best, some very
Prepared using sagej.cls
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Figure 2. This is a global representation of the data produced by considering a 10 percent subsample of all the causes identified
by the causation tracker on the set of comments. It treats statements as nodes of a network and two statements are linked if they
share the same lemma (the number of shared lemmata corresponds to the link weight). In this analysis, only nouns, verbs and
adjectives are considered (the text processing is done with spaCy (Honnibal and Montani 2019)). For this global view, each cause
statement is labeled by that word within the statement that is most frequent in all the data. The visual output was created using the
network exploration tool Gephi (0.92). The 2D layout is the result of the OpenOrd layout algorithm integrated in Gephi followed by
the label adjustment tool to avoid too much overlap of labels.
general hypotheses about the types of opinions that figure in
the climate change debate.
Micro-level investigations: opinions on nuclear power
and global warming Based on the range of topics on
which beliefs are expressed, a micro-level analysis can be
conducted to reveal what those beliefs are and, for instance,
whether they align or contradict each other. This can be
achieved by singling out a cause of interest, and mapping
out its associated effects.
As revealed by the global overview of the climate change
opinion landscape, a portion of the debate concerns power
and energy sources. One topic with a particularly interesting
role in this debate is nuclear power. Figure 3illustrates how
a more detailed representation of opinions on this matter
can be created by spatially representing all of the effects
associated with causes containing the expression ‘nuclear
power’. Again, similar beliefs (in terms of words used in the
effects) are positioned closer to each other, thus facilitating
the detection of clusters. Commenters on The Guardian for
instance express concerns about the deaths or extinction that
might be caused by this energy resource. They also voice
opinions on its cleanliness, whether or not it might decrease
pollution or be its own source of pollution, and how it
reduces CO2-emissions in different countries.
Whereas the detailed opinion landscape on ‘nuclear
power’ is relatively limited in terms of the number of mined
opinions, other topics might reveal more elaborate belief
Prepared using sagej.cls
8Preprint
deaths
only the nuclear industry can do this
bqstart really every clean energy issues is not hampered by greenpeace
now they support re+storage would destroy their credibility
less land and water pollution than solar or wind power
any significant risk to life
membership of any green organization automatically qualifies you
even your beloved germany has n't stopped building coal plants having no choice
that it ca nt be correct
a massive backlash against nuclear power
it is the nuke power industry 's get
the pollution
calamitous human health problems
windmills are a pointless waste of money
the french have lower per co2 emissions in part
2 wo n't happen
so long as it is wearing its nuclear hat and all for giving it all the subsidies it is asking for
france has per capita carbon emission that are only 2/3 of germany
al gore lost the presidential election 12 years ago
nuclear war and possible mass extinction of life on earth
we 'll need far more than we imagine
i said hydro
no it is n't it 's
it 's easy for france
the only reason the news story ever got so massive is
that 's
my views on independence only concern me on this
right
they use much lessfossil fuels
the us navy is only interested
which is absolutely necessary to reduce carbon emissions
on the other hand france with 100 % nuclear power has to import electric power every winter from germany
china can cut emission
while ensuring consistent stringent safety standards for the next generation of nuclear power stations
to quote him spent is one less dollar spent on clean renewable energy and one more dollar spent on making the world a comparatively dirtier and a more dangerous place
this situation is unavoidable
death of the entire biosphere
Figure 3. A detailed representation of effect statements
associated with nuclear power. Clusters concern potential
extinction or deaths, notions of cleanliness and pollution, and
the reduction of CO2 levels in different countries. Labels
represent the full output of the semantic frame extractor.
systems. This is for instance the case for the phenomenon of
‘global warming’. As shown in Figure 4, opinions on global
warming are clustered around the idea of ‘increases’, notably
in terms of evaporation, drought, heat waves, intensity of
cyclones and storms, etc. An adjacent cluster is related to
‘extremes’, such as extreme summers and weather events,
but also extreme colds.
From opinion observation to debate
facilitation
The observatory introduced in the preceding paragraphs
provides preliminary insights into the range and scope
of the beliefs that figure in climate change debates on
TheGuardian.com. The observatory as such takes a distinctly
descriptive stance, and aims to satisfy, at least in part, the
information needs of researchers, activists, journalists and
other stakeholders whose main concern is to document,
investigate and understand on-line opinion dynamics.
However, in the current information sphere, which is marked
by polarization, misinformation and a close entanglement
with real-world conflicts, taking a mere descriptive or
neutral stance might not serve every stakeholder’s needs.
Indeed, given the often skewed relations between power and
information, questions arise as to how media observations
might in turn be translated into (political, social or economic)
action. Knowledge about opinion dynamics might for
instance inform interventions that remedy polarization or
disarm conflict. In other words, the construction of (social)
media observatories unavoidably lifts questions about the
possibilities, limitations and, especially, implications of the
machine-guided and human-incentivized facilitation of on-
line discussions and debates.
Addressing these questions, the present paragraph
introduces and explores the concept of a debate facilitator,
that is, a device that extends the capabilities of the previously
discussed observatory to also promote more interesting and
constructive discussions. Concretely, we will conceptualize
a device that reveals how the personal opinion landscapes of
commenters relate to each other (in terms of overlap or lack
thereof), and we will discuss what steps might potentially
be taken on the basis of such representation to balance the
debate. Geared towards possible interventions in the debate,
such a device may thus go well beyond the observatory’s
objectives of making opinion processes and conflicts more
transparent, which concomitantly raises a number of serious
concerns that need to be acknowledged.
On rather fundamental ground, tools that steer debates
in one way or another may easily become manipulative
and dangerous instruments in the hands of certain interest
groups. Various aspects of our daily lives are for instance
already implicitly guided by recommender systems, the
purpose and impact of which can be rather opaque. For
this reason, research efforts across disciplines are directed
at scrutinizing and rendering such systems more transparent
(Milano et al. 2019). Such scrutiny is particularly pressing
in the context of interventions on on-line communication
platforms, which have already been argued to enforce
affective communication styles that feed rather than resolve
conflict. The objectives behind any facilitation device should
therefore be made maximally transparent and potential
biases should be fully acknowledged at every level, from
data ingest to the dissemination of results (for a thorough
discussion of challenges facing social media research
in a post-truth era, see Rogers 2018). More concretely,
the endeavour of constructing opinion observatories and
facilitators foregrounds matters of ‘openness’ of data and
tools, security, ensuring data quality and representative
sampling, accounting for evolving data legislation and
policy, building communities and trust, and envisioning
beneficial implications. By documenting the development
process for a potential facilitation device, the present paper
aims to contribute to these on-going investigations and
debates. Furthermore, every effort has been made to protect
the identities of the commenters involved. In the words of
media and technology visionary Jaron Lanier, developers
and computational social scientists entering this space
should remain fundamentally aware of the fact that ‘digital
information is really just people in disguise’ (Lanier 2013,
19).
With these reservations in mind, the proposed approach
can be situated among ongoing efforts that lead from debate
observation to facilitation. One such pathway, for instance,
involves the construction of filters to detect hate speech,
misinformation and other forms of expression that might
render debates toxic (see for instance De Smedt et al. 2018;
Van Hee et al. 2018). Combined with community outreach,
language-based filtering and detection tools have proven to
raise awareness among social media users about the nature
and potential implications of their on-line contributions
(see Grey 2019). Similarly, advances can be expected from
approaches that aim to extend the scope of analysis beyond
descriptions of a present debate situation in order to model
how a debate might evolve over time and how intentions of
the participants could be included in such an analysis.
Progress in any of these areas hinges on a further
integration of real-world data in the modelling process,
as well as a further socio-technical and media-theoretical
investigation of how activity on social media platforms
and technologies correlate to real-world conflicts. The
remainder of this section therefore ventures to explore how
Prepared using sagej.cls
9
incrementally higher temperatures with each year fractionally hotter than the last
but it wo n't be
the recent droughts and storms
the snows of kilimanjaro have been disappearing not but because of climate change that occurred during the little ice age
to pull out of coastal areas
that arctic ice is not melting
australia is being burnt to cinders
this storm
an increase in vulcanism
it's
increased hurricanes.cyclones or extreme weather
the extra heat
the idea of the disappearing snow on
climate change and climate disruption
bqstart
an increase in forest fires
2011 11:18am bqstart climate change
the floods in australia
to expect warm balmy winters in britain
the floods
this extreme weather
trying to frighten people into paying higher taxes
how many deaths
extreme cold
high temperatures or low temperatures
now they have already melted and can not do so
an additional 2.8 months a year of heat waves
that when the weather is warmer it is and when the weather is cooler it is due to climate change bqend
climate change
increased temperatures
these fires
which change of climate
heat waves or changing drought and flooding patterns bqend
i never ruled out the possibility of other places becoming although this is more correlation rather than causation
that do n't have the possibility in ending
the southern ocean lows that bring rain to the darling catchment to stay further south reducing rainfall
that extra heat
climate scientists are not exaggerating the dangers of climate change
weather systems to migrate toward the poles
reading the scientific literature about the rate and mechanisms of glacial ice cap melting
the warmer climate of britain
the destruction of vast tracts of the great barrier reef
the drying of the southern australian deserts and agricultural lands
that's got nothing to do with climate change
as mass coral bleaching killed half of it
the bleaching and subsequent death of a large fraction of the coral on the great barrier reef over recent years has been
i ask you know for the third time what do you mean by catastrophes in the context of climate change
the last two years
increased temperatures and increased evaporation rates resulting in a drying of the fuel load
more damage plus the amount of rain causing floods and runoff damage
the increased frequency of droughts
higher temperatures and more heat waves increasing evaporation
increased extreme weather events
people
the cloud layer being too high above the plateau too often for the high altitude moist subtropical forest and the nothofagus forest
weather patterns to move poleward
an increase in the number of very destructive high category cyclones
a month long arctic blast
that occur
the increased number of heat waves
old people would have been killed by that prolonged cold snap
extra heat waves
an increase in the number of heat waves in australia
to reduce the changes in the climate by reducing carbon emissions
the one solving poverty with changing the climate."the climate is changing
land masses
warmer oceans and more heat waves leading to more coral bleaching episodes
increased number of higher category 4 and now 6 cyclones which produce the strong wave action which physically destroys large sections of reef smashing it into coral rubble
an increase in global temperatures
increased numbers of extreme el nino events
an earlier longer fire season or period of the year in the south of the continent
which has experienced an marked increase in heat waves as a result of the greenhouse effect
polar vortices due to the reduced high latitude temperature gradient
the increased evaporation
the extra heat waves
more heat waves which in the drier enso phases leads to more evaporation and resultant decline in soil moisture
the opposite phases of enso to be more extreme
more extreme opposite phases of enso resulting in more severe droughts and more severe
the discovery of thicker ice in on the antarctic has not necessarily been
a single fire or fire season or unusual hot weather
more severe el nino phases of enso such malaria epidemics
more severe el nino events
drought in the southern half of the continent
to climate change
xx degrees temperature increase due to yy increase in co2 concentration
that the permafrost soils could melt and thus release the methane hydrates
wetter winters
that any one particular event occurred or was even made worse by it
the snow
extreme hot extreme cold
at least the freezing poor in germany might be a little warmer
more extreme storms
an event
this fire
increasing
now those caps are reducing
so any changes in weather during the past 18 years can not be since the globe is n't even warming
when the oceans warm up
the disruption
a single weather event
to release copious amount of now frozen methane at the great depths of our ocean sediments
the melting of ice caps in greenland and antarctica
record low temperatures across many areas of the united states this past winter
those cold parts are getting smaller this can be checked out via oceanic people
an increased frequency of devastating drought
the price of food goes up pay
more ice
the ice in antarctica is increasing
an increase in storm intensity
that snow will happen less often in the uk but it will still happen and cause chaos in twenty years time
the measurement of higher temperatures
someone claiming that snowpack in the alps is declining
bqstart i ask you now for the third time what do you mean by catastrophes in the context of climate change
changes in oceanic currents
nationally to demand the u.s. # actonclimate an climate change
but according to the climate models most scientists think are accurate parts of asia and africa that are currently inhabited will become this hot
that have become extinct in the last 50 years incontrovertibly
weather changes that will destabilize more populations and reduce our living space
climate damage
extreme cold snaps as the oscilates
a greater likelihood of extreme summers in some locations
more drought
if that's not
the thousands of australians especially the elderly induced heart attacks this coming summer
an increase in global temperature
the damage
much of their poverty and loss of their farms
high temps
Figure 4. A detailed representation of the effects of global warming. This graph conveys the diversity of opinions, as well as
emerging patterns. It can for instance be observed that certain opinions are clustered around the idea of ‘increases’, notably in
terms of evaporation, drought, heat waves, intensity of cyclones and storms, etc. An adjacent cluster is related to ‘extremes’, such
as extreme summers and weather events, but also extreme colds. Labels represent the full output of the semantic frame extractor.
conceptual argument communication models for polarization
and alignment (Banisch and Olbrich 2018) might be
reconciled with real-world data, and how such models might
inform debate facilitation efforts.
Debate facilitation through models of alignment and
polarization As discussed in previous sections, news
websites like TheGuardian.com establish a communicative
settings in which agents (users, commenters) exchange
arguments about different issues or topics. For those seeking
to establish a healthy debate, it could thus be of interest to
know how different users relate to each other in terms of
their beliefs about a certain issue or topic (in this case climate
change). Which beliefs are for instance shared by users and
which ones are not? In other words, can we map patterns of
alignment or polarization among users?
Figure 5ventures to demonstrate how representations of
opinion landscapes (generated using the methods outlined
above) can be enriched with user information to answer such
questions. Specifically, the graph represents the beliefs of
two among the most active commenters in the corpus. The
opinions of each user are marked using a colour coding
scheme: red nodes represent the beliefs of the first user, blue
nodes represent the beliefs of the second user. Nodes with a
green colour represent beliefs that are shared by both users.
Taking into account again the factors of aggregation that
were discussed in the previous section, Figure 5supports
some preliminary observations about the relationship
between the two users in terms of their beliefs. Generally,
given the fact that the graph concerns the two most active
commenters on the website, it can be seen that the rendered
opinion landscape is quite extensive. It is also clear that the
belief systems of both users are not unrelated, as nodes of all
colours can be found distributed throughout the graph. This
is especially the case for the right-hand top cluster and right-
hand bottom cluster of the graph, where green, red, and blue
nodes are mixed. Since both users are discussing on articles
on climate change, a degree of affinity between opinions or
beliefs is to be expected.
Upon closer examination, a number of disparities between
the belief systems of the two commenters can be detected.
Considering the left-hand top cluster and center of the
graph, it becomes clear that exclusively the red commenter
is using a selection of terms related to the economical
and socio-political realm (e.g. ‘people’, ‘american’, ‘nation’,
‘government’) and industry (e.g. ‘fuel’, ‘industry’, ‘car’,
etc.). The blue commenter, on the other hand, exclusively
engages in using a range of terms that could be deemed more
technical and scientific in nature (e.g. ‘feedback’, ‘property’,
‘output’, ‘trend’, ‘variability’, etc.). From the graph, it also
follows that the blue commenter does not enter into the red
commenter’s ‘social’ segments of the graph as frequently
as the red commenter enters the more scientifically-oriented
clusters of the graph (although in the latter cases the red
commenter does not use the specific technical terminology of
the blue commenter). The cluster where both beliefs mingle
the most (and where overlap can be observed), is the top right
cluster. This overlap is constituted by very general terms (e.g.
‘climate’, ‘change’, and ‘science’). In sum, the graph reveals
that the commenters’ beliefs are positioned most closely to
each other on the most general aspects of the debate, whereas
there is less relatedness on the social and more technical
aspects of the debate. In this regard, the depicted situation
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10 Preprint
Figure 5. A representation of the opinion landscapes of two active commenters on TheGuardian.com. Statements by the first
commenter are marked with a blue colour, opinions by the second commenter with a red colour. Overlapping statements are
marked in green. The graph reveals that the commenters’ beliefs are positioned most closely to each other on the most general
aspects of the debate, whereas there is less relatedness on the social and more technical aspects of the discussion.
seemingly evokes currently on-going debates about the role
or responsibilities of the people or individuals versus that of
experts when it comes to climate change (see for instance
Katz 2016;M¨
aki 2019;Fibieger Byskov 2019).
What forms of debate facilitation, then, could be based
on these observations? And what kind of collective effects
can be expected? As follows from the above, beliefs
expressed by the two commenters shown here (which are
selected based on their active participation rather than
actual engagement or dialogue with one another) are to
some extent complementary, as the blue commenter, who
displays a scientifically-oriented system of beliefs, does not
readily engage with the social topics discussed by the red
commenter. As such, the overall opinion landscape of the
climate change could potentially be enriched with novel
perspectives if the blue commenter was invited to engage
in a debate about such topics as industry and government.
Similarly, one could explore the possibility of providing
explanatory tools or additional references on occasions
where the debate takes a more technical turn.
However, argument-based models of collective attitude
formation (M¨
as et al. 2013;Banisch and Olbrich 2018) also
tell us to be cautious about such potential interventions.
Following the theory underlying these models, different
opinion groups prevailing during different periods of a
debate will activate different argumentative associations.
Facilitating exchange between users with complementary
arguments supporting similar opinions may enforce biased
argument pools (Sunstein 2002) and lead to increasing
polarization at the collective level. In the example considered
here the two commenters agree on the general topic, but
the analysis suggests that they might have different opinions
about the adequate direction of specific climate change
action. A more fine–grained automatic detection of cognitive
and evaluative associations between arguments and opinions
is needed for a reliable use of models to predict what would
come out of facilitating exchange between two specific
users. In this regard, computational approaches to the
linguistic analysis of texts such as semantic frame extraction
offer productive opportunities for empirically modelling
opinion dynamics. Extraction of causation frames allows
one to disentangle cause-effect relations between semantic
units, which provides a productive step towards mapping
and measuring structures of cognitive associations. These
opportunities are to be explored by future work.
Conclusion
Ongoing transitions from a print-based media ecology to
on-line news and discussion platforms have put traditional
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11
forms of news production and consumption at stake. Many
challenges related to how information is currently produced
and consumed come to a head in news website comment
sections, which harbour the potential of providing new
insights into how cultural conflicts emerge and evolve. On
the basis of an observatory for analyzing climate change-
related comments from TheGuardian.com, this article has
critically examined possibilities and limitations of the
machine-assisted exploration and possible facilitation of on-
line opinion dynamics and debates.
Beyond technical and modelling pathways, this examina-
tion brings into view broader methodological and epistemo-
logical aspects of the use of digital methods to capture and
study the flow of on-line information and opinions. Notably,
the proposed approaches lift questions of computational
analysis and interpretation that can be tied to an overarching
tension between ‘distant’ and ‘close reading’ (Moretti 2013).
In other words, monitoring on-line opinion dynamics means
embracing the challenges and associated trade-offs that come
with investigating large quantities of information through
computational, text-analytical means, but doing this in such
a way that nuance and meaning are not lost in the process.
Establishing productive cross-overs between the level of
opinions mined at scale (for instance through the lens of
causation frames) and the detailed, closer looks at specific
conversations, interactions and contexts depends on a series
of preliminaries. One of these is the continued availability
of high-quality, accessible data. As the current on-line media
ecology is recovering from recent privacy-related scandals
(e.g. Cambridge Analytica), such data for obvious reasons
is not always easy to come by. In the same legal and
ethical vein, reproducibility and transparency of models
is crucial to the further development of analytical tools
and methods. As the experiments discussed in this paper
have revealed, a key factor in this undertaking are human
faculties of interpretation. Just like the encoding schemes
introduced by Axelrod and others before the wide-spread use
of computational methods, present-day pipelines and tools
foreground the role of human agents as the primary source
of meaning attribution.
Acknowledgements
<This project has received funding from the European Unions
Horizon 2020 research and innovation programme under grant
agreement No 732942 (Opinion Dynamics and Cultural Conflict in
European Spaces – www.ODYC CE US.eu).>
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In recent years, online social networking has revolutionized interpersonal communication. The newer research on language analysis in social media has been increasingly focusing on the latter's impact on our daily lives, both on a personal and a professional level. Natural language processing (NLP) is one of the most promising avenues for social media data processing. It is a scientific challenge to develop powerful methods and algorithms which extract relevant information from a large volume of data coming from multiple sources and languages in various formats or in free form. We discuss the challenges in analyzing social media texts in contrast with traditional documents. Research methods in information extraction, automatic categorization and clustering, automatic summarization and indexing, and statistical machine translation need to be adapted to a new kind of data. This book reviews the current research on NLP tools and methods for processing the non-traditional information from social media data that is available in large amounts (big data), and shows how innovative NLP approaches can integrate appropriate linguistic information in various fields such as social media monitoring, healthcare, business intelligence, industry, marketing, and security and defence. We review the existing evaluation metrics for NLP and social media applications, and the new efforts in evaluation campaigns or shared tasks on new datasets collected from social media. Such tasks are organized by the Association for Computational Linguistics (such as SemEval tasks) or by the National Institute of Standards and Technology via the Text REtrieval Conference (TREC) and the Text Analysis Conference (TAC). In the concluding chapter, we discuss the importance of this dynamic discipline and its great potential for NLP in the coming decade, in the context of changes in mobile technology, cloud computing, virtual reality, and social networking. In this second edition, we have added information about recent progress in the tasks and applications presented in the first edition. We discuss new methods and their results. The number of research projects and publications that use social media data is constantly increasing due to continuously growing amounts of social media data and the need to automatically process them. We have added 85 new references to the more than 300 references from the first edition. Besides updating each section, we have added a new application (digital marketing) to the section on media monitoring and we have augmented the section on healthcare applications with an extended discussion of recent research on detecting signs of mental illness from social media.
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The outcome of the UK's referendum on whether the UK should leave or remain in the European Union (so-called Brexit) came as a jolt to many across Europe. In this paper, we use causal mapping from soft OR to analyse longitudinal data from nine televised Brexit debates spread across the 4 weeks leading up to the referendum. We analyse these causal maps to build one view on why Brexit happened. The maps are analysed for the breadth, depth and consistency of arguments in the debate and, broadly, finds that the Leave campaign focused more consistently on a smaller set of campaign themes, contributed more detail to those themes, and focused on their own core issues rather than being diverted onto Remain strongholds. In contrast, Remain shared more information but across a broader range of themes (meaning they were less consistent), and followed Leave into themes that were clearly not their core battleground. The novelties for soft OR in this paper include: the difficulties of building and validating causal maps from secondary data; new techniques for analysing a group of causal maps to uncover the properties of arguments that spread longitudinally through a campaign; a methodology for a teaching case using publicly availability data; linking the paper, philosophically, to critical realism given the unique dataset. Finally, we identify differences in the Leave and Remain debate campaigns to offer one answer to the question ‘Why did Brexit happen?’.
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This book outlines a new approach to the analysis of decision making based on "cognitive maps." A cognitive map is a graphic representation intended to capture the structure of a decision maker's stated beliefs about a particular problem. Following introductory chapters that develop the theory and techniques of cognitive mapping, a set of five empirical studies applies these new techniques to five policy areas. Originally published in 1976. The Princeton Legacy Library uses the latest print-on-demand technology to again make available previously out-of-print books from the distinguished backlist of Princeton University Press. These paperback editions preserve the original texts of these important books while presenting them in durable paperback editions. The goal of the Princeton Legacy Library is to vastly increase access to the rich scholarly heritage found in the thousands of books published by Princeton University Press since its founding in 1905.
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How and Why Groups Polarize