=Paper=
{{Paper
|id=Vol-3671/paper11
|storemode=property
|title=Computational Narrative Framing: Towards Identifying Frames through Contrasting the Evolution of Narrations
|pdfUrl=https://ceur-ws.org/Vol-3671/paper11.pdf
|volume=Vol-3671
|authors=Markus Reiter-Haas,Beate Klösch,Markus Hadler,Elisabeth Lex
|dblpUrl=https://dblp.org/rec/conf/ecir/Reiter-HaasKHL24
}}
==Computational Narrative Framing: Towards Identifying Frames through Contrasting the Evolution of Narrations==
https://ceur-ws.org/Vol-3671/paper11.pdf
Computational Narrative Framing: Towards
Identifying Frames through Contrasting the Evolution
of Narrations
Markus Reiter-Haas1,⇤ , Beate Klösch2 , Markus Hadler2 and Elisabeth Lex1
1
Graz University of Technology, Institute of Interactive Systems and Data Science, 8010 Graz, Sandgasse 36/III
2
University of Graz, Department of Sociology, 8010 Graz, Universitätsstraße 15/G4
Abstract
Our understanding of the world is fundamentally shaped by language, with narrations being a central
point, and influenced by its framing. Recent advancements in language models gave rise to computational
methods for both narrative understanding and framing analysis. Although given their overlap, these two
strands are mostly researched independently. In this position paper, we argue for their consolidation
in the form of narrative framing, i.e., the framing process driven by narrations. Herein, we outline
similarities between both based on semantic elements. Besides, we discuss how different narratives
might compete with each other, as well as evolve over time. Thereby, narratives inevitably change the
framing, exemplarily depicted on the issue of climate change. We believe that the analysis of narrative
frames will lead to a broader understanding of textual corpora as a whole rather than individual pieces
of text.
Keywords
Framing Theory, Narrative Frames, Competing Narrations, Climate Change Framing, Semantic Graphs
1. Introduction
Experiences in the real-world and narrative perception are inextricably linked in humans,
even on a neurological level [1]. In a similar vein, the framing of narratives can act as a
device to blend fiction and reality [2], consequently suggesting certain solutions to specific
problems [3] and affect the people’s choices [4]. Unlike other types of frames, the pool of
options concerning narratives for framing is essentially endless. Although some works on
computationally extracting narrative framing have already been conducted [e.g., 5, 6, 7], the still
sparse body of research tends to favor one strand of research, i.e., either narrative understanding
or framing analysis, over the other.
In: R. Campos, A. Jorge, A. Jatowt, S. Bhatia, M. Litvak (eds.): Proceedings of the Text2Story’24 Workshop, Glasgow
(Scotland), 24-March-2024
⇤
Corresponding author.
� reiter-haas@tugraz.at (M. Reiter-Haas); beate.kloesch@uni-graz.at (B. Klösch); markus.hadler@uni-graz.at
(M. Hadler); elisabeth.lex@tugraz.at (E. Lex)
� https://iseratho.github.io/ (M. Reiter-Haas); https://elisabethlex.info/ (E. Lex)
� 0000-0001-9852-8206 (M. Reiter-Haas); 0000-0002-8061-6088 (B. Klösch); 0000-0002-0359-5789 (M. Hadler);
0000-0001-5293-2967 (E. Lex)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
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In this position paper, we present a basic theoretical framework for computational narrative
framing analysis, effectively combining computational narrative understanding and computa-
tional framing analysis research. We identify the commonalities between the two strands to
form an elementary understanding of the necessities for emerging approaches in this direction.
Moreover, we explore how such a framework enables contrasting the evolution of different
lines of narrative frames across important issues. Herein, we exemplarily discuss the narrative
change regarding climate change, i.e., the evolution from global warming to the more urgent
naming of climate catastrophe and similar [8].
As our main contribution, we want to provide an impulse towards further exploration of how
narrations are being used to frame long-term discourses. We hope that our work bridges the
gap between two similar but still distinct communities.
2. Background
The present work comprises two strands of computational research, based on narrations and
frames, respectively. Specifically, we focus on the parts where computational narrative under-
standing and computational framing analysis mostly overlap.
Computational Narrative Understanding (CNU) Narrations, being defined by their con-
tent and structure, are used to study many topics, with the policy process in the narrative policy
framework being a well-known example [9]. Herein, the elements of narrativity have first been
fully formalized by Piper et al. [10], with the minimal definition being structured as "Someone
tells someone somewhere that || someone did something(s) [to someone] somewhere at some time
for some reason". Here, the left part (before the ||) is the perspective of narrating the story, while
the right part concerns the story itself (i.e., diegesis). In a similar vein, some strides have already
been made towards analyzing narrative frames [5]. Overall, we observe that actors and events
are central components of narrations, which provides overlap with some computational framing
analysis approaches.
Computational Framing Analysis (CFA) Framing deals with salience in communication [3]
and is concerned “how” a text is presented rather than “what” is apparent [11]. The analysis of
framing can be seen as a task of natural language understanding (e.g., similar to tasks in the
GLUE benchmark [12]). The notion of framing is very distinctively conceptualized in compu-
tational literature, comprising supervised and unsupervised, as well as mixed-method based
approaches [11]. As supervised approaches depend on corpora and codebooks, unsupervised
approaches are more in line with narrative understanding. For instance, DiMaggio et al. [13]
use topic modeling for framing analysis and equate certain topics with frames. Besides, they
define frames as comprising narratives among other cues, and also find narratives as being
part of a particular topic. Other works consider semantic information, such as semantic role
labels [6] and semantic graphs [7] to analyze narratives directly.
In the remainder of the paper, we use the theory presented by Piper et al. [10] for CNU and
the survey by Ali and Hassan [11] for CFA as cornerstones in their respective areas. Also,
130
when using the term narrative framing, we refer to the framing using narratives as device, thus
compounding both CNU and CFA. Herein, we focus on framing through semantic structure
(i.e., following Fillmore and Baker [14]) rather than other forms of framing.
3. Computational Narrative Framing
As a starting point for better understanding narrative framing, we analyze how their research
directions are entangled. Both, Piper et al. [10] and Ali and Hassan [11], identify a set of future
research endeavors by stating core challenges and open questions, respectively. We provide
an overview of these future directions in Table 1. Comparing them, we observe remarkable
overlap between the two strands that we summarize as key requirements.
First (R1), there is the improvement of methods by considering fine-grained nuanced features,
e.g., latent features (CNU) and semantic relations (CFA). Herein, CNU focuses on understanding
deep stories via narrative structuring of higher-order organizing principles, while CFA focuses
on semantic relations going beyond words with the aim to better explore frames. Here, we
identify narrative structure as a key direction for future research.
Second (R2), the relation between multiple documents (potentially even for distinct types)
for a broader understanding are established. CNU aims to understand narrative discourse by
studying the interaction of narrative features, even between different narrative products (e.g.,
movies vs. books). CFA questions how different documents can be connected or inform each
other. We reason that the understanding of narratives must go beyond individual narratives
and shift towards a focus on competing narratives.
Third (R3), both emphasize the incorporation of more nuanced knowledge sources, e.g., past
events like wars (CNU), culture, and omission (CFA). CNU argues for more robust classification
of narrative types via interdisciplinary large-scale registers. CFA calls for a computational
model to construct frames via salience through various framing devices. We see the modeling
of the temporal evolution as a good starting point to capture more nuances.
Based on these suggestions, we reason that computational narrative framing approaches
must go beyond simple feature analysis (e.g., on the word-level) of individual documents,
but rather analyze the corpus as a whole considering the nuances within. Specifically, we
argue that narrative frames emerge from the temporal evolution of collections of documents
comprising structural elements. In the following, we aim to synthesize these requirements from
the bottom-up.
Table 1
Overview of core challenges in CNU [10] and open question in CFA [11].
CNU core challenges CFA open questions (abbreviated) Synthesized requirements
R1 Narrative beliefs Capture all relevant semantic relations? Narrative Structure
R2 Narrative responses Frames across multiple documents? Competing Narratives
R3 Narrative economies Salience through framing devices? Temporal Evolution
131
Semantic Frames U.N. Secretary-General Antonio Guterres said on Monday (December 11th, 2023) one key to success of the Concepts
say-01 COP28 climate summit (in Dubai) was for nations to reach agreement on the need to "phase out" fossil fuels. person
key-02 secretary-general
succeed-01 nation
reach-01 agree cop28 summit climate
NOT
agree-01 Successful fuel fossil
Monday COP28 climate summit U.N. Climate Policy
(December 11th, 2023) Antonio Guterres Countries Fossil Fuels
need-01 (in Dubai) organization
U.N. Secretary-General
phase-01
(v6 / say-01 :ARG0 (v4 / person :name (v5 / name :op1 "Antonio" :op2 "Guterres") :wiki "Antonio_Guterres")
Context :ARG1 (v3 / secretary-general) :ARG1 (v20 / nation :location (v19 / -rrb- :location (v17 / city :name (v18 / name Named Entities
:op1 "Dubai") :wiki "Dubai")) :domain (v11 / key-02 :quant 1 :ARG1 (v10 / -rrb-) :ARG1 (v12 / succeed-01 Antonio_Guterres
city
:ARG1 (v13 / cop28 :mod (v16 / -lrb- :mod (v15 / summit :mod (v14 / climate)))))) :ARG0-of (v21 / reach-01
Dubai
date-entity
:ARG1 (v22 / agree-01 :ARG1 (v23 / need-01 :ARG1 (v25 / phase-01 :ARG1 (v24 / ``) :ARG1 (v28 / fuel :mod
monday (v27 / fossil) :mod (v26 / '')))))) :time (v9 / date-entity :day 11 :month 12 :year 2023) :poss (v8 / -lrb- :time (v7 / United_Nations
monday))) :ARG0 (v1 / organization :name (v2 / name :op1 "U.N.") :wiki "United_Nations"))
(a) Narrative Framing Representation (b) Temporal Evolution
Figure 1: Exemplary plot on how narratives on climate change could be depicted.
3.1. Narrative Structure
To start, we establish narrative frames that go beyond word frequency, with structure being a
focal point. We base the analysis on our prior work [7] using semantic graphs based on abstract
meaning representations [15].
In Figure 1a, we depict an example that shows how complex such representations can be,
even for short sentences. Specifically, we used a sentence from a recent news article on the
COP281 :
U.N. Secretary-General Antonio Guterres said on Monday (December 11th, 2023)
one key to success of the COP28 climate summit (in Dubai) was for nations to reach
agreement on the need to "phase out" fossil fuels.
We transformed the text to a graph using [16]2 and present its linearized form for brevity. While
a detailed explanation is beyond the scope of this paper3 , the key elements that such model
extracts are semantic frames (comprising verbs and senses) [14], concepts (nouns), contextual
information (time and location), as well as named entities. We want to highlight that the
model implicitly performs both simplifications (e.g., singularization of nations to nation) and
generalizations (e.g., wikification of U.N. to United Nations), potentially in unison (e.g., stemming
and verbification of agreement to agree-01), to improve the resulting representations.
Therefore, this or similar representations are necessary to fulfill the first requirement for
computational narrative framing (R1). Note that, we used a straightforward parser here for
demonstration, but more recent language models, e.g., BART [17], might be better suited for
the task at hand.
3.2. Competing Narratives
After having extracted the narratives of individual documents, we might compare them. In
most scenarios, narratives will cluster together and compete with each other, with narratives
1
Taken from: https://www.reuters.com/business/environment/phasing-out-fossil-fuels-is-key-cop28-success%
2Dsays-uns-guterres-2023-12-11/ where we enhanced the text with meta-data from the article, i.e., time and
location, which we put in parentheses.
2
Available as open tool at: https://bollin.inf.ed.ac.uk/amreager.html
3
The guidelines are available at: https://github.com/amrisi/amr-guidelines/blob/master/amr.md
132
of conspiracy theories being an obvious instance. Especially regarding the topic of climate
change, conspiracy thinking seems larger than anticipated [18]. Even for COP28, conspiracy
narratives are spreading, such as relating to the fear of keeping the population captive4 . Besides
considering conspiracies, many intra- and inter-corpus dependencies should also be considered,
with polarization [19] being another noteworthy example.
To identify such competing narratives, we can rely on established methods for corpus analysis
(e.g., [20]). However, beyond applying them on lexical features (e.g., words), considering the
semantic level as established in 3.1 is important for the second requirement (R2).
3.3. Temporal Evolution
While the third requirement (R3) contains many distinct points, we focus on the temporal
aspects that we see as the most common factor. Hence, the present should depend on the past,
while also account for irregularities like notable omissions of specific narratives. Furthermore,
the evolution will depend on the competing narratives established in 3.2. For example, the
overall narrative framing might show a similar trend but at a different pace depending on the
cultural context, which we visually illustrate using an artificial example in Figure 1b. Notably,
certain events could lead to sudden shifts in trajectories that need to be accounted for.
While methods like time-series analyses seems sound at first glance, we believe that due to
discreteness of narrative frames, sequential modeling approaches [21] are a better fit. In such
models, side-information such as relevant events could be utilized as well.
3.4. Challenges in Narrative Framing Analysis
Foremost, we acknowledge that the main challenges identified still remain unsolved. Beyond
that, detecting narrative frames is even more difficult to achieve than both CNU and CFA
individually. While data is sparse in both domains, there is a complete lack of ground truth data
to train algorithms for predicting the narrative framing. Moreover, classical machine learning
setups like classification would not work at all, as there is no complete set of narrative frames
due to their emergent properties. Finally, the validation, especially quantitatively, is unsolved
as the evolving nature of narrative frames hinders most (static) measures.
4. Learning from Evolving Narratives: The Case of Global
Warming to Climate Catastrophe
Following up on the topic of the example provided in Figure 1, we now briefly discuss how
considering computational narrative framing would support understanding the discourse on
climate change. The framing of climate change has gradually shifted from global warming
to climate change, and more recently towards climate crisis or even climate catastrophe [8].
While anecdotally obvious, such patterns are notoriously challenging to detect computationally
when they are not known in advance. Climate change, in particular, is a long-term issue where
changes are noticeable even for laymen. Besides the reframing of the scientific consensus
4
https://phys.org/news/2023-11-climate-conspiracy-theories-flourish-cop28.html
133
towards increasing urgency, even the framing of climate change denial shifted their narrations
from outright denying climate change to denying human-made climate change. Supporting
such discourse analysis with computational methods would be very beneficial for identifying
narrative patterns for preemptive counteraction, as well as future predictions.
5. Conclusion
In this paper, we introduced computational narrative framing that combines the research of
computational narrative understanding with computational framing analysis. Herein, we identi-
fied that both of their pressing future research directions overlap, which coincidentally situate
the main requirements for the task at hand. Specifically, (i) narrative structure, (ii) competing
narratives, and the (iii) temporal evolution are fundamental for a thorough understanding.
We exemplarily support our reasoning concerning the evolution of competing narrations in
climate change discourse. Our hope is that this paper serves as a starting point for mutual
benefit between two distinct research strands that enables a broader understanding of important
societal topics.
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