Conflicting narratives and polarization on social media
Armin Pournaki
1 Max Planck Institute for Mathematics in the Sciences, Leipzig, Germany 2 Laboratoire Lattice, École Normale Supérieure - PSL - CNRS - Univ. Sorbonne Nouvelle, Montrouge, France 3 médialab, Sciences Po, Paris, France
July 22, 2025
Abstract
Narratives are key interpretative devices by which humans make sense of political reality. In this work, we show how the analysis of conflicting narratives, i.e. conflicting interpretive lenses through which political reality is experienced and told, provides insight into the discursive mechanisms of polarization and issue alignment in the public sphere. Building upon previous work that has identified ideologically polarized issues in the German
Twittersphere between 2021 and 2023, we analyze the discursive dimension of polarization by extracting textual signals of conflicting narratives from tweets of opposing opinion groups. Focusing on a selection of salient issues and events (the war in Ukraine, Covid, climate change), we show evidence for conflicting narratives along two
dimensions: (i) different attributions of actantial roles to the same set of actants (e.g. diverging interpretations of the role of NATO in the war in Ukraine), and (ii) emplotment of different actants for the same event (e.g. Bill Gates in the right-leaning Covid narrative). Furthermore, we provide first evidence for patterns of narrative alignment,
a discursive strategy that political actors employ to align opinions across issues. These findings demonstrate the use of narratives as an analytical lens into the discursive mechanisms of polarization. 1 Introduction
Narratives are key interpretive devices through which humans make sense of their environment. They serve as lenses through which reality is experienced and told. In political science, narratives are described as meaning-making devices through which “people make sense of their lives” by “construct[ing] disparate facts and weav[ing] them together cognitively to make sense of reality”. This sense-making stepis often achieved by reducing the complexity of complex political events and relationships into “easily understandable stories”. This reduction, which usually involves causal chaining of events and attributions of archetypical roles to political actors, such as protagonists or antagonists, allows the narrator to convey their political stance by selecting which events are included, which actors and relationships are highlighted and what role they play. In political communication, this makes narratives powerful rhetorical devices that have been shown to affect beliefs and attitudes. Furthermore, narratives can serve as ways to define one’s identity, and the identity of a group by delimiting the “we” from an outside “they”. These identities, often defined through antagonization of an out-group, could be at the origin of larger political divides. Next to identities, Friedman suggests that ideological polarization can be explained through the existence of diverging interpretive frames through which the world is experienced. We argue that narratives are a powerful analytical lens through which one can observe such diverging interpretive frames in text. This in turn provides insight into the central fault lines along which ideologically opposing camps divide. Only few studies have investigated the connection between polarization and narratives empirically. Jing and Ahn shed light on the role of diverging narratives in the context of the Covid debate on Twitter in the US by computationally analyzing narratives of Democrats and Republicans. They find that (i) narratives diverge in focus, Republicans focusing on outside actors such as China, while the Democrats focus on health issues and financial support, and that (ii) both parties heavily antagonize each other in their narrative. Still within the topic of Covid, Zhao et al. show that the Twitter discourse on Covid in the US has become more polarized and aligned with party preferences over time. This points to another feature of narratives: the one of integrating disparate political events into pre-defined webs of meaning, and potentially instrumentalizing issues such as a pandemic to further fortify one’s ideological views through strategic emplotment. Directly related is the question whether such strategies are actually successful in affecting opinions and beliefs.
Antinyan et al. show in an experimental setting that selective exposure to different narratives about Covid may indeed affect participants’ beliefs about the origins of the pandemic. The present paper goes beyond previous approaches by systematically analyzing the conflicting narratives of opposing opinion groups across several salient political topics. Drawing upon previous work that empirically provides structural evidence for polarization and issue alignment in the German Twittersphere between 2021 and 2023, we systematically extract and analyze textual signals of narratives of opposing ideological camps around the war in Ukraine, Covid and climate change in order to shed light on the following questions: What are the central discursive fault lines along which the left and right-leaning groups divide? What discursive strategies are employed in order to connect different issues such to facilitate the alignment of opinions? Approaching these questions using a combination of distant and close reading, this work surfaces the discursive points of tension between the two groups and reveal larger metanarrative patterns that could be at the origin of the observed issue alignment. 2 Data
This work uses a dataset of tweets previously introduced in Pournaki et al.. The dataset includes tweets on trending topics over the course of more than two years. More specifically, the authors collected tweets from 2021-03-29 to 2023-07-12 according to the following scheme: at the beginning of each day, a script was launched that collects the current “trending topics” (from now on referred to as “trends”) in Germany using the Twitter Trend API. By default, trends are personalized based on the account’s Twitter/X usage. One can, however, disable the personalization by setting a specific location from which to draw the trending topics, which then yields “popular topics among people in a specific geographic location”. The script was re-run every 15 minutes. At the end of each day, the authors counted the number of times each trending topic appeared during the day and kept the top 5 most frequent ones. This provided a proxy of the five most important trending topics for that day. The Twitter Search API was used to collect German-speaking tweets using the exact trend keyword as a query on the day it trended and the day after. Tweets were collected using a single Twitter API key, collecting tweets for maximally 24 hours every day. Each collected trend is one sub-dataset. This results in 3007 trending topics collected. Trends were merged if the exact same phrase occurs within a time window of 1 day. After merging, there are 2693 sub-datasets.
These contain a total of 19,105,532 tweets. 3 Methods
3.1 Structural analysis of polarization
The structural analysis of polarization on Twitter was previously presented in Pournaki et al. and is described in the upper two rows of Fig. 1. We summarize it very briefly here in order to contextualize the present findings. 3.1.1 Extracting issues from tweets
First, all tweets were processed through a BERTopic-based pipeline which allows to inductively surface the latent topics present in the text by clustering tweets in a semantic space given Figure 1: Analysis pipeline. The raw text data of tweets is processed in a topic model to extract the main issues discussed, which is then used to assign an overall issue to each trend. In parallel, retweet networks are constructed and clustered to extract the opinions of users for each trend. From this set of partitions, the most prominent users are extracted (influencers and multipliers) and a user alignment matrix is computed that quantifies how systematically pairs of users appear in the same cluster across the dataset. Importing the issue labels from the topic model analysis allows to compute the user alignment separately for each issue, which is then used to generate the issue alignment matrix that measures how similarly two issues sort these users into opinion groups. The results from the structural analysis of retweet networks allows to divide the textual corpus into ideological clusters. After processing the tweets using the narrative pipeline described in Pournaki and Willaert, we systematically compare the actantial networks to uncover conflicting narratives for a selected range of salient topics (War in Ukraine, Covid and climate change). by pre-trained sentence embedding models. The assumption is that clusters in that space correspond to topic clusters. These topics were then manually grouped into larger issues, such as Covid (including topics such as vaccination, masks, school closures), or climate change (including topics such as activism, meat consumption, ...). Each trend could then be assigned one of those larger issues, based on the majority of tweets it contains. Table A1 shows the issues extracted through this method and their prevalence across the corpus. 3.1.2 Extracting opinions from tweets
In parallel, a retweet network was extracted from each trend, where nodes are Twitter accounts and a directed link is drawn from account i to j if i retweets j . Since retweets are considered endorsement,
densely connected clusters in such networks correspond to opinion clusters in the underlying political debate. Conceptually, a retweet network with two clusters can therefore be treated as an opinion poll. A user can take either the stance
´ 1 or 1 on a given issue spanned by a trending topic. An SBM-based approach provides the cluster assignment for each node in each network. This allows to measure, across the dataset, how often a given pair of users are in the same opinion cluster. Computing a measure of user alignment and clustering the resulting alignment matrix shows that the user base is largely divided into two camps: one left-leaning and one right-leaning, which were identified by manually examining the most active users in each camp. The global clustering into left/right is used in order to assign each tweet to one of the two camps, based on the majority of retweets it recieved. This allows to generate, for each issue, two sub-corpora: one of left-leaning and one of right-leaning tweets, that are analyzed separately for conflicting narrative signals. 3.2 Extracting conflicting narratives
In order to analyze conflicting narratives of various topics in Twitter discussions, this work builds upon the extraction of narrative signals developed in Pournaki and Willaert. The central idea is that narratives are co-created and often not explicitly stated in full in the text. Therefore, the challenge of extracting narratives from digital traces is to identify key narrative signals, in the form of actors, their relationships, and their goals/motives. These signals are then reassembled into actantial networks, where nodes are actors, and links denote the nature of their relationship as conflictive, supportive, or neutral. These networks are typically very large and contain information that is not necessary for the analysis of political narratives. To get insight into the most prominent narrative signals, the nodes in the networks are therefore filtered using centrality measures (in-/out-degree, betweenness). Finally, we recall that each edge in the network arises from a collection of textual passages in the original data . Pointing precisely to these passages allows to contextualize each of those actantial links and add a hermeneutic layer to the computational analysis. The approach therefore combines a fully inductive, machine-learning based extraction of narrative signals from raw text with an interpretive layer of close reading, which allows to reconstruct the underlying (meta-)narratives.
In practice, the approach consists in first transforming text into a structured representation using the semantic parsing framework of Abstract Meaning Representation (AMR). This framework represents the meaning of sentences as directed acyclical graphs, where the root is the verb of the sentence, and the leaves are given by the arguments (e.g. subject/object). The corpus is therefore parsed into a set of AMR graphs, which are then transformed into a tabular representation, which can easily be queried for narrative signals, and from which actantial networks can be generated for a detailed description of the processing steps involved). Since the AMR parser in its current state does not handle German, we by translate our full corpus of original tweets into English using the transformer-based model mbart-large-50-many-to-many-mmt, that provides a good tradeoff between efficiency and accuracy. We then parsed the left and right-leaning subcorpora to analyze the systematic differences in narratives of the opposite camps across topics, with respect to involved actors, their ascribed actions, as well as relationships in the form of actantial networks. A link from node i to j in the actantial network has a weight w pi, j q which denotes the number of times a relationship from i to j has been (re)tweeted in the corpus, and a score σ pi, j q Pr´ 1 , 1 s which denotes how supportive, conflictive or neutral the relationshipis. This score is computed based on all the individual interactions from i to j, which, in are classified using verb category dictionaries. In this work, we extend the narrative signal extraction to allow for the systematic extraction of conflicting narratives, and to better fit the Twitter data at hand, by adding the following two processing steps:
3.2.1 LLM-assisted actantial link labeling
In the actantial network, each node is an actor and a directed, weighted, scored link is drawn from actor i to j where the weight is defined as the sum of retweets of all tweets in which the edge is contained, and the score denotes whether the link is supportive (positive values), conflictive (negative values) or neutral (zero). In Pournaki and Willaert, the approach to label a given relationshipis based on a predefined categorization of verb families using the VerbAtlas dictionary, where each verb family was assigned one of the three categories . This bears a number of limitations which mainly revolve around the question of context, which potentially plays an important role in the determination of the conflictive or supportive nature of the relationship. In the present setting, the context consists of the actors involved, and the preceding and following sentences in the tweet. We identify Large Language Models (LLMs) as a potential tool to include this broader context into the
1 The sentence “Russia attacked Ukraine.” would, for instance, contribute to a conflictive link from Russia to Ukraine . 2 3 The Verbatlas family attackbomb, which contains verbs like “invade” would, for example, be labeled as conflictive. analysis. Previous work has shown their potential in text annotation tasks, and in particular that their accuracy can exceed the one of human annotators, even though this depends on the domain. Aside from providing the full tweet as context to the LLM, this approach allows to use the original, German-speaking tweet for the inference of the relation type. This can mitigate potential mistakes in the translation that could have occurred at an earlier step of the pipeline. We use the open weight model Phi-4, forcing the model to produce JSON output using structured/guided generation, which highly facilitates further processing and makes the pipeline more efficient by decreasing inference time.
We report the prompt in the Appendix A.2 and validate and assess the benefits and limitations of the LLM-based approach in the Appendix A.3. 3.2.2 Systematic extraction of conflicting narratives
We approach the extraction of conflicting narratives by systematically comparing links in actantial networks. Let G l p V l , E l q be the actantial network extracted from left-leaning tweets and Gr p V r , Er q the actantial network extracted from right-leaning tweets. Take two edges e 1 “ pi, j, w l pi, j q , σ l pi, j qq PE land e 2 “ pi, j, w r pi, j q , σ r pi, j qq PEr, where w denotes the weight and σ Pr´ 1 , 1 s denotes the edge score. We assume conflicts between two narratives to surface when relationships between actors are narrated in an opposite way (i.e. supportive in one narrative vs. conflictive in the other). Therefore, if signp α l pi, j qq ‰ signp α r pi, j qq, we assume the underlying edge to correspond to a narrative trace that points to a fault line in the underlying debate. Finally, we examine the actantial networks made of only those links that point to potential conflicts. Paired with a close reading of relevant tweets associated to those links, the approach allows to reconstruct the diverging lenses through which the two camps view political reality. 4 Results
We focus on a subset of prevalent issues during the data collection period, namely the war in Ukraine, Covid and climate change. The goal of this analysis is twofold: (i) discover the underlying conflicting narratives that might explain the observed polarization, and (ii) discover the discursive mechanisms by which these different issues may be connected. Using the methodology described in 3.2, we start by analyzing each of the three issues separately, and then move to a combined analysis that reveal first evidence of larger meta-narrative patterns. We start by analyzing each of the three issues separately. We generate the subset of tweets of each camp and extract the actantial networks. We start by apprehending identity narratives as out-going ego networks of the node we for both camps. Then, we extract conflict networks, subsets of the actantial networks that contain the most central actants, and that solely contain links that have an opposing score between the two camps. These conflict networks surface the central fault lines in the polarized discussions. These networks can be concieved as re-assembled narrative signals that, by themselves, provide a distant overview of the narrative content in the underlying textual corpus, and surface the discursive points of tension between the communities. To fully apprehend the underlying narratives, it is necessary to complement the network representations with a close reading approach of a subset of relevant tweets that led to the extracted links. In the following section, we demonstrate this guided close reading protocol: first, we extract the different types of narrative networks (ego networks of the nodes we and networks of most important actors for both camps). Then, we contextualize the links by reading and interpreting the 5 most retweeted tweets of each link (see Appendix for a selected list of close-read tweets). Finally, we summarize the conflicting narratives for each of the three investigated issues, and provide first insights into the common patterns observed across them that may explain the observed issue alignment. Ukraine
freedom
NATO
Nord Stream 2 supplies gas
Ukrainians
support
delivery
attack
defense
demonstration
weleft
Vladimir Putin weapon tank weright
Ukraine
peace
Europe
pipeline
link score
Russians
war
Russia
-1 0 +1
Figure 2: Identity narrative network extracted from trends related to Russia’s aggression of Ukraine. Link width
“ “ “ is proportional to weight, link color reflects the link score (red conflictive, blue supportive, grey neutral, color bar only plotted here for subsequent actantial networks). Links reflect relationships retweeted at least 500 times. 4.1 War in Ukraine
One central topic in the timerange we observed was the Russian invasion of Ukraine. Russia began increasing its military presence near the border from March/April 2021, before launching the invasive attack on Ukraine’s territory on February 24 2022. This has since led to the deadliest war in Europe since World War II, causing large numbers of military and civilian casualties and displacements. The war is also fought on the level of (mis)information and narrative, which this section sheds light on by systematically analyzing the narrative signals of left- and right-leaning Twitter communities. 4.1.1 Identity narrative
Figure 2 shows the merged ego-networks of the node we for the topic Ukraine. The links are filtered by the sum of the number of (re)tweets that contain the relationship: each edge needs to be retweeted at least 500 times. We see that certain nodes are only connected to either one of the we entities, while certain nodes are connected to both. The former represent actants emplotted in relation to only one of the groups, while the latter represent actants to which relationships can be contested across the two
groups.
Examining the network and close reading the most retweeted tweets associated to each edge allows us to reconstruct the narratives built around each of the collective identities. The left-leaning campidentifies itself through strong solidarity and support for Ukraine (supportive links towards Ukrainians, support, defense ), which implies delivering weapons to Ukraine. Vladimir Putin is strongly condemned, the corresponding tweets show that he is explicitly blamed for starting the war. Furthermore, we observe a conflictive stance towards demonstrations, which refer to Ukraine right
war
USA
media
NATO
Germany
Poland
Europe
the West
Olaf Scholz
Ukraine
Russia
gas
arms
peace
Vladimir Putin
war
USA
media
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Germany
left
arms Europe Russia the West
Olaf Scholz
Ukraine
supplies
law
children
AfD
peace
Vladimir Putin supplies
law
children
The Green Party
lie
freedom
EU Russians NS2 [tank]
Poland freedom EURussians NS2 tank
Figure 3: Conflict networks extracted from trends related to Russia’s aggression of Ukraine. Each link has opposite sign in the left/right network. “demonstrations for peace” organized by groups with pro-Russian tendencies in Germany. Turning to the nodes connected to the right-leaning we, we observe supportive relationships to freedom and Nord Stream 2 . Freedom is often evoked in right-wing narratives, and in the context of the Ukraine war it is connected to freedom of action (not to enter the war with Russia), and freedom of expression (for non-interventionist voices promoting a pro-Russian narrative). We observe conflictive relationships to NATO and Europe, which are considered culprits in pushing Russia into an unavoidable defense war (described more explicitly in the following subsection). Looking at the contested nodes between the two camps, we observe that both parties exhibit a supportive relationship to peace . The tweets connected to the link reveal different conceptions as to how to reach peace. On the left, peace can only be reached by full support for Ukraine, while the right promotes the possibility of peace through non-intervention and reiterated phrases like “Weapons cannot bring peace.”. Looking at the connections to war, the left-leaning narrative exposes the shortcomings and lacking sanctions and deals that allow to “finance Putin’s war” (hence the non-negative link). The right-leaning camp on the other hand strictly opposes Germany’s involvement in the war. 4.1.2 Conflicting narratives
Figure 3 shows the actantial network of central nodes in both left and right-leaning camps. Only links with different sign of scores for left/right are drawn. This highlights the fundamental interpretive differences of political events between the two camps. While, on the left, the USA and NATO are trying to help Ukraine who is being attacked by Russia, the right-leaning campidentifies them as the main culprits in this war. This difference leads to other rifts, such as the divisive stance towards weapon supplies insinuated by the difference in connecting weapons and peace: in the right-leaning cluster, weapons can never lead to peace, whereas the left-leaning cluster has a more nuanced stance and supports weapon supplies to protect Ukraine’s sovereignty. Furthermore, we observe that both sides blame the ideological opponents, such as the media and the Green
Party by the right, and the AfD by the left.
This example shows how the same event can serve to narrate an actant’s position in opposite ways, depending on which position fits the larger ideological narrative. 4.2 Covid
We now turn our attention to another central trending topic in the dataset: the Covid pandemic. The first trending hashtag in the dataset appears on 2021-03-30 (“#harterLockdownJetzt”) and the last on 2023-06-17 (“Lauterbach”). Across the timerange, we find discussions around the various facets of the pandemic, from counter-measures like lockdowns, schools closures, home office to prevention like masks, testing and vaccination. These sparked polarized debates about individual freedom, trust in science and the government. 4.2.1 Identity narrative
Figure 4 shows the identity narrative network around the Covid pandemic. We observe that the leftleaning cluster identifies itself through the positive stance towards measures to prevent virus spread. This includes masks, tests, lockdowns, restrictions, and vaccinations . The right-leaning camp strongly opposes these measures while referring to fundamental human rights and misguided, politicized science. There is a focus, in the left-leaning narrative, on solidarity and compliance to fight the disease together, while the right-leaning narrative focuses on individual freedom . These differences in narrative turn masks or vaccines into markers of ideological identity: wearing masks, getting vaccinated can act as signals of ideological leaning. left
covid test pandemic mask vaccination life
freedom
lockdown
Long Covid
vaccine
children
doctor
school
government
politician Karl Lauterbach Jens Spahn
Covid right
right
covid test
vaccination
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pandemic mask
vaccine
doctor
government
fear
freedom
Long Covid
children
school
Bill Gates
science
safety
Bundestag
side effects politician
Karl Lauterbach
Figure 5: Conflict networks extracted from trends related to Covid. 4.2.2 Conflicting narratives
Figure 5 shows the conflict networks for the Covid pandemic. We observe divergent views on the effectiveness of the vaccination and its side-effects, and trust in science . The vaccine’s safety is seriously doubted in the right-leaning narrative, while it is considered a necessary solution to fight the pandemic on the left. We observe the interpretation of masks and other measures as harmful attacks on fundamental rights . Different political actors are antagonized: on the left, Jens Spahn for his allegedly poor management of vaccine distribution, and on the right, Karl Lauterbach for his strong pro-vaccine stance and his alleged attacks on freedom and fundamental rights . Looking at children, we observe differences in what each camp considers harmful for them: the left-leaning camp warns of “sacrificing” children by keeping schools open and increasing their chances of being infected, while the right-leaning camp warns of the potential psychological harms connected to masks and isolation. We also observe certain actors that only play a role in the right-leaning narrative: for one, fear is being mongered by politicians in order to get the population to comply with the harsh measures. Then, science proves the importance of the vaccine’s side-effects, which are not discussed in the left-leaning camp. Finally, we find traces of the conspiracy theory around Bill Gates ’ involvement in the orchestration of the pandemic for personal profit, which does not exist in the left-leaning narrative. In the right-leaning camp, we observe traces of a recurring general narrative about individual freedom: in this case, it is infringed upon by the political establishment through harsh measures. This theme also connects Covid to the topic of climate change, as will become visible in the following section. 4.3 Climate change
The climate change topic is a multi-faceted issue in the dataset and spans trends from the beginning of the data collection on 2021-03-29 to 2023-07-12. The related sub-topics are on one hand weather events such as the heavy floods in July 2021, discussions about speed limits on highways, climate activism, the climate summits, and the town of Lützerath that was destroyed to build a coal mine and sparked large-scale demonstrations. The subsequent analysis shows that there are fundamental differences between the two camps as to how extreme weather events, as well as climate change and climate activism, are interpreted and emplotted. Climate Change
temperature
CO2
obligation
climate crisis
climate
climate changeprotection
violence
politics
destruction
protest
weleft
climate catastrophe
limit
coal
heat
policy
car
state
storm
weright climate terrorists
insect
meat
Figure 6: Identity narrative network extracted from trends related to climate change. Link weight threshold 250. 4.3.1 Identity narrative
Figure 6 shows the identity narrative networks of climate change. One main difference between the leftleaning and the right-leaning camp arises from the interpretation of climate change as an actual threat. This shows itself through the use of the word climate catastrophe . From this standpoint, the left-leaning cluster derives a negative stance towards coal, a positive stance towards speed and CO 2 emission limits, and climate protests . On the right, the role of CO 2 is relativized, and the emphasis is laid on the illegal activities of climate terrorists . Furthermore, there is a focus on the consumption of meat, induced by the fear that the supermarket chain Lidl announced an increase in plant-based products (the negative link arises from sentences like “We will have to renounce meat”). Along similar lines, we find traces of a conspiracy narrative around political elites forcing the regular population to eat insects while they themselves eat steaks on their yachts.
This echoes previously identified narratives around individual freedom that is being infringed upon by political elites. 4.3.2 Conflicting narratives
Figure 7 shows the conflict networks of climate change. We observe a central difference between the two camps with respect to the impact of climate change on society. While the left considers floods a direct consequence of climate change, the right does not acknowledge this connection. Similarly, we observe differences in the evaluation of the dangers of coal . Staying within the right-leaning narrative, we observe that the Green Party and the fear-mongering media are antagonized, as in the war in Ukraine. Lidl is evoked in the right-wing narrative due to allegations that the supermarket chain will reduce sales of meat products. Furthermore, one central interpretive conflict lies in the role Germany can play in the larger scheme of climate change : for the right, Germany cannot respond to stop climate change and should therefore focus on other issues, while the left believes in Germany’s responsibility to tackle the issue. This may lead to conflicting conceptions about the Last left freedom climate change
CO2 protection coal flood RWE authority
Climate Change right climate change
CO2
protection
flood
authority
climate
Lidl
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water disaster
Germany
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Last Generation
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Germany
Last Generation climate activist
limit
climate crisis
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politician
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Armin Laschet man FDP
state [violence] The Green Party
The Green Party
Figure 7: Conflict networks extracted from trends related to climate change. Generation and climate activists . On the right, their actions are heavily condemned as illegaland antidemocractic, while the left supports their efforts. Finally, we note that climate change is not a pressing issue for the right, while the left is convinced that it is one of the central issues that need to be tackled. The analysis of conflicting narratives for each of the three issues revealed the central fault lines along which the debates are polarized. While these fault lines are usually topic-specific, such as the diverging interpretations of NATO’s role in the war in Ukraine, the guided close reading analysis surfaced some elements common to all three issues, such as antagonizations of the media in the right-leaning cluster. The next section shall provide a first approach to analyze the narrative elements that are used to bind issues together and that may be at the origin of the observed issue alignment. 4.4 Issue alignment
We use the insights gained from the analysis of individual issues to generate first hypotheses about the role of narratives for issue alignment.
One way in which observe issues to be aligned are through the invokation of recurring actors as protagonists or antagonists. Focusing on the connection between climate change and Covid, we find that the left-leaning camp systematically holds up their trust in science :
“Why does politics not listen to science?? #Laschet #Hochwater disaster #Climate disaster #Climate crisis”
“I’ve been vaccinated 4 times, I’ve vaccinated over 3,000 patients in my clinic, I’ve set up a fever ambulance and I’m doing social media education. #DasHabeIchGetan I want politics to listen to science and provide me with vaccines.
I don’t play golf. « NatalieGrams: Uncle brings me an idea. So write down under DasHabeIchGetan what you’ve done to slow down the pandemic. Maybe politics will finally understand what it has to do! — »”
““Politics must finally learn to face science on an eye-to-eye basis.” — Christian Althaus, Bernese epidemiologist, after leaving the Corona Taskforce of the Federal Government.”
On the right, we observe that (mainstream) science is discredited:
“What if politics and science are on the same side of climate change as Corona?”
We also observe signals of recurring narratives around fear and freedom that connect issues together.
This leads to analogies between the government’s approach to Covid and climate change policy, as exemplified through the emerging keyphrase “climate lockdown”, a powerful rhetorical tool that binds the issues
together:
“Please tell me that the people are not so submissive again and would accept a climate lockdown?!”
“Are you already looking forward to the CO2 lockdown and the QR code on your phone, without which you will never be able to go anywhere? #Lanz #RicardaLang @ZDF #GreenGarbage”
On both camps, we observe the antagonization of ideologically opposite political parties. On the left, the
AfD is constantly evoked as a threat to Germany, while on the right, the Green Party (often mentioned as the “prohibition party”, highlighting their politics as a threat to individual freedom) plays that role as the central antagonist. This is exemplified through the villanization of the Green politician Karl Lauterbach
as a central antagonist in several topics:
“The stupid Karl #Lauterbach announces a heat protection plan, although much more people die in cold than in heat. Therefore, a cold protection plan would make more sense: #Open Nordstream2 and #Restart nuclear power plants.”
Furthermore, we observe the general distrust in media in both topics:
“Fact: During #Corona, we were deceived, framed by the media, trumpeted by purchased experts... Other opinions (which were the right ones) were suppressed, made impossible, shamed...
EXACTLY THE SAME takes place again on the topic of #Climate Change! #Berlin2030”
“Here speaks a courageous nurse from New Zealand. ‘No one listens to us and the media doesn’t listen to us either. They say we would complain of misinformation. We see vaccine damage every day. People contact us all the time because of their vaccine damage.’”
Finally, we observe examples in which a larger number of issues are explicitly aligned:
“Never thought how fast people swallow absurdities like \"weapons create peace\". But clearly, mask was also freedom #b2502 #peace #Corona #propaganda”
“There are no women with penises. Climate change is not man-made. Unregulated migration is destroying our social system and endangering our everyday lives. Corona is an average disease. Vaccination is ineffective but not without side effects.
With 70% in taxes and levies, the state is taking almost all of the income from working people. Ukraine is not defending us or our “values”. Every now and then such banal truths have to be spoken in the face of the flood of disinformation.”
“How did people actually survive in the past, when there were no smart-ass recommendations for meat consumption, Karl Lauterbach wasn’t panicking about the heat, nobody was constantly talking about CO2
and people were still allowed to decide for themselves which heating systems to installand which cars to buy?”
These examples allow to generate hypotheses about certain meta-narratives that may guide the ideological narratives of the two camps. On the right, we observe a strong emphasis on individual freedom and reluctance to governmental interference. Furthermore, the skeptical narrative of the “lying media” and “fear-mongering politicians” transcends topical boundaries. On the left, we observe a recurring theme of solidarity and shared responsobility – with the Ukrainian people, risk groups in Covid, the protesters in
Lützerath – that may serve as a larger ideological meta-narrative. These observations are a first approach at using the narrative lens to study how issues are aligned through emplotment. In the examples shown for the war in Ukraine, Covid and climate change, we observe a combination of repeatedly antagonized actors and direct analogies that may serve as strategies to align issues discursively and bundle them into coherent ideological sets. 5 Discussion
The present work has analyzed the discursive dimension of polarized debates through the lens of conflicting narratives. On a large dataset of tweets from trending topics in Germany between 2021 and 2023, where previous structural analyses have revealed strong polarization and issue alignment, this work investigates the discursive dimension by extracting the conflicting narratives of opposing opinion groups. Focusing on three strongly salient topics (the Russian invasion of Ukraine, the Covid pandemic and climate change), it demonstrated – using the guided close reading method of extracting and reassembling narrative signals developed in – how opposing opinion groups diverge in their interpretations of the same political events and issues. In particular, two ways in which narratives about the same political issue can be conflicting were identified: (i) they can invoke the same actors, but attribute different relationships to them, or, (ii) they can invoke different actors altogether, showing a divergence in the attribution of actantial importance within topics. The first type of conflicting narrative is the one we largely observe in the case of the Russian invasion of Ukraine. The central actors (e.g. Ukraine, Russia, Germany, the USA, NATO, weapon supplies) are present in both narratives, but the role, in particular of NATO and the USA are diametrically opposite. While these actors want to stop the war in the left-leaning narrative, they are fueling it in the right-leaning narrative. Similarly, the elemental connection between human activity and climate change is disputed between the two sides in the issue of climate change. The second type of conflicting narratives, the omission and highlighting of different actors, can be also observed across the analyzed issues. In the narrative of Russia’s invasion, the right invokes the actor Nord Stream 2, which is barely mentioned on the left. Similarly, vaccination side effects play a small role in the left-leaning narrative on the Covid pandemic, as does Bill Gates. Looking at the narratives on climate change, speed limits and the events in Lützerath play a marginal role in the right-leaning narrative, which instead focuses on alleged threats by the supermarket chain Lidl to the personal freedom to eat meat. These findings echo a central feature of political narratives: only elements that serve the purpose of “fram[ing] [one’s own] political positions as favorable” are emplotted.
Analyzing the common narrative signals across the three topics, we find first evidence for the potential relationship between narratives and issue alignment. We observe a recurrence of certain antagonized actors and analogous relationships across the three topics we investigated (e.g. threats to Germany by certain political parties, threats to individual freedom by policies...). Connecting these findings to previous literature, we recall recent work by Lejano and Nero analyze the narratives of climate change skeptics, where they observe a meta-narrative around the loss of individual freedom through climate policies. This is in line with the findings in our work, where we observe this pattern in the right-leaning camp for the topic of Covid (through measures) and Ukraine (Germans not being free to choose to intervene or not in the war). On the left, we observe signs of a meta-narrative around solidarity and shared responsibility that connects the three observed topics (solidarity with Ukrainian people, Covid risk groups, climate activists). What remains to be investigated more thoroughly is how to model such broader meta-narrative patterns, thus allowing for a systematic analysis across a wider range of topics present in our dataset. Such models could potentially build on structuralist approaches such as the one by Greimas that would encompass the notion of “genetic metanarratives” hypothesized by Lejano and Nero. Comparing climate skeptical narratives with anti-immigration narratives, the authors find the recurring pattern of an outside actor (“the eco-terrorist”, “the foreigner”) threatening to transform a desired “traditional way of life”. Further developing methods for the detection of existing, and the discovery of new such patterns can provide additional insights into the role of narrative in ideological polarization and issue alignment.
Aside from the potential for more comprehensive conceptualand computational models for metanarratives, the present work has limitations that call for future work. Firstly, the analysis of conflicting narrative networks assumes an internal coherence of narratives within the opinion groups. This must not necessarily be the case, especially for topics like the war in Ukraine, which has shown to divide the German Left party. Furthermore, we recall here that we only investigated the links for which the communities’ narratives differed – looking at those parts of the narratives that are similar could provide a potential for mediation across camps. Additionally, narratives usually evolve over time, an aspect also left unconsidered so far. In future work, actantial networks can be analyzed over time to detect narrative shifts, for example through changes in link scores, or the introduction of new actors to the plot. This would allow deeper empirical insights into “how narrative truth is fabricated, how the validity of narratives is constructed or deconstructed, and how dif f erent modes of reception influences the felicity conditions of political narratives.”. Reflecting on the general appropriateness of actantial networks as representations of narratives, we may question whether the relationships they encode (supportive/conflictive/neutral) are complex enough to capture the potential nuances present in ideological narratives. While the full spectrum of verb actions (for instance, given by Verbatlas) is too extensive, there may be an intermediary level of verb categorization that could be envisaged in future work. Furthermore, additional work shall be done investigating the construction of in-groupidentities. This work has provided first steps towards this analysis by investigating nodes connected to a common “we”. We identify several natural avenues of improvement: firstly, the pronoun “we” can be decomposed into its different deictic facets (inclusive, exclusive, general). Secondly, the extraction and labeling of actantial links can be sharpened by adding the dimension of the speaker: is the speaker part of the “we” that is evoked?
This distinction would improve the direct interpretability of the actantial networks. Furthermore, more thorough analysis needs to be conducted to analyze the systematic antagonization of the out-group. While investigating the actantial ego network of the pronoun “they” could be a first approach, this would require to be complemented with coreference resolution to remove textual traces that do not correspond to out-group descriptions. Reflecting on the representativity of the findings, we highlight that the present work has focused the analysis on one social media platform, Twitter/X, in a single country, Germany. Future work shall extend to different social media platforms and traditional media, as well as include different countries. The mathematical object of the actantial network lends itself to a systematic analysis of narrative diffusion through measures of graph similarity, which can yield insights into the role of different platforms and media sources play in distributing or influencing leading narratives around different issues. To conclude, the observations presented in this work provide empirical descriptions of previously mentioned functions of narratives in conveying apolitical agenda. By providing a lens through which political reality is interpreted, they reduce the complexity of political crises like war, pandemics, or climate change, by selecting specific events and actors to highlight, and assigning antagonist/protagonist roles to them. These findings suggest further evidence for the manifestation of an “epistemological crisis” postulated by Friedman, where opposing groups diverge with respect to their interpretation of political reality. If both groups assume that their narrative is a “true” representation of reality, this can only lead to mistrust and antagonization of the out-group. This work presented first attempts to empirically address these questions by providing a description of these diverging interpretive lenses in the form of conflicting narratives. Acknowledgements
The author thanks Eckehard Olbrich for fruitful discussions. The author acknowledges funding from the European Union’s Horizon Europe framework under project number 101094752 ), and from the French government under management of Agence Nationale de la Recherche as part of the “Investissements d’avenir” program, reference ANR19-P3IA-0001.