N arrat EX Dataset: Explaining the Dominant Narratives in News Texts (2025)

Abstract: We present NarratEX, a dataset designed for the task of explaining the choice of the Dominant Narrative in a news article, and intended to support the research community in addressing challenges such as discourse polarization and propaganda detection. Our dataset comprises 1,056 news articles in four languages, Bulgarian, English, Portuguese, and Russian, covering two globally significant topics: the Ukraine-Russia War (URW) and Climate Change (CC). Each article is manually annotated with a dominant narrative and sub-narrative labels, and an explanation justifying the chosen labels. We describe the dataset, the process of its creation, and its characteristics. We present experiments with two new proposed tasks: Explaining Dominant Narrative based on Text, which involves writing a concise paragraph to justify the choice of the dominant narrative and sub-narrative of a given text, and Inferring Dominant Narrative from Explanation, which involves predicting the appropriate dominant narrative category based on an explanatory text. The proposed dataset is a valuable resource for advancing research on detecting and mitigating manipulative content, while promoting a deeper understanding of how narratives influence public discourse.

## introduction the internet has become a powerful medium for disseminating information, but it has also amplified the spread of polarized content and deceptive and manipulated narratives. this is particularly evident in contentious topics such as geopolitical conflicts and global challenges like climate change, where dominant narratives -central perspectives or storylines-, play a significant role in shaping public opinion and fostering division. extremely biased and polarized discourse occurs in different regions and cultures and can be observed practically in any language found online @xcite @xcite . the intricacies of writing can sometimes obscure the intended message, especially in cases of disinformation or highly biased political content. therefore, making the narrative more explainable is essential for mitigating confirmation bias and strengthening readers' capacity to interpret a given piece of text @xcite . therefore, understanding and analyzing these narratives is critical for identifying disinformation and propaganda, and for addressing their potential societal impact. despite the growing interest in narrative analysis @xcite , there is a lack of comprehensive multilingual datasets that address the challenges of narrative classification and explanation in diverse linguistic and cultural contexts. existing corpora @xcite @xcite are often limited to specific aspects, such as reading comprehension or causal reasoning, and do not capture the interplay between narrative categorization and explanatory reasoning. to address this gap, we introduce nar-ratex, a dataset designed to advance research in identifying and explaining dominant narratives and sub-narratives. the dataset consists of 1,056 news articles in four languages (bulgarian, english, portuguese, and russian), covering the ukraine-russia war (urw) 1 and climate change (cc). each article in our dataset is labeled with a dominant narrative (spanning 21 classes), a subnarrative (spanning 74 sub-classes), and an explanation that justifies their selection for the given article. our dataset uniquely bridges the gap between narrative classification and explanatory reasoning, enabling a deeper understanding of how disinformation and propaganda are framed, spread, and interpreted online. our contributions are as follows: ## related work explaining text narratives has become particularly relevant in domains like disinformation and propaganda detection, where understanding the deeper structure of narratives is essential for reliable analysis. since these tasks are challenging even for humans, automated models need not only accurate narrative classification, but also interpretable justifications for their predictions. as a result, several datasets have become vital in natural language processing (nlp) for tasks like machine reading comprehension @xcite @xcite ), text summarization @xcite , and explainable artificial intelligence (ai) @xcite . these datasets have provided resources to train machine learning models that predict outcomes, reason explicitly, and explain their decision-making processes, ultimately enhancing our understanding of narrative structures. for instance, the narrativeqa dataset @xcite offers detailed question-answer pairs focused on story comprehension, supporting tasks such as contextual reasoning and summarization. the tellmewhy dataset @xcite focuses on causal reasoning by providing explanations for the events that occur in a narrative, enhancing a model's ability to identify cause-and-effect relationships. other datasets such as narrasum @xcite and summscreen @xcite have further contributed by pairing narrative texts with their summaries, assisting models in learning to condense and to reframe narrative content. additionally, rhetorical analysis datasets from shared tasks like semeval and checkthat! @xcite @xcite have provided annotations that highlight persuasive strategies and logical fallacies, enabling models to identify narrative techniques, particularly in argumentative or manipulative content. among resources emphasizing explainability, the e-snli dataset @xcite stands out by adding human-written justifications to natural language inference labels, thus guiding models toward transparent entailment reasoning. while the above-described datasets from previous work have enhanced interpretability, they have often focused on specific reasoning tasks such as entailment, causality, or provide explanations indirectly through summarization or questionanswer formats. in contrast, our proposed dataset introduces a novel approach by directly linking each narrative-labeled text to a concise, humanannotated explanation that explicitly justifies both the dominant narrative and the sub-narrative assignments. unlike current datasets that focus on outcomes such as summaries or event-based explanations, our resource bridges narrative understanding with interpretability by presenting the rationale behind narrative categorization itself. this design enables a deeper exploration of the interplay between narrative structure and its communicative function, supporting models that not only identify narratives, but also explain why a particular narrative interpretation is plausible. for these reasons, our dataset makes a unique contribution to the field of explainable narrative analysis, particularly relevant in various applications requiring high-stakes narrative reasoning, such as misinformation analysis. we hope that it will represent a valuable resource for studying narratives and interpretability of misinformation detection. ## experiments and evaluation to showcase the potential of the narratex dataset, we conduct two experiments on two possible tasks where the dataset can be used. the first is the generation of the narrative explanation for the dominant narrative and sub-narrative. the second is a multiclass classification task that aims to classify each explanation into the proper dominant narrative. for each task, we provide random and zero-shot baselines based on open and closed generative llms and their respective results. for the multiclass task, we also added a finetuned bert model. since the majority of the approaches in both tasks are unsupervised/zero-shot, we present the evaluation results in both the test set as well as in the entirety of the dataset (train+test). ## conclusion and future work we introduced narratex, a new multilingual dataset designed to facilitate research on dominant narrative and sub-narrative classification and explanation in several european languages. the dataset uniquely bridges the gap between narrative categorization and explanatory reasoning. our analysis highlights the dataset's potential to support a range of tasks, from narrative explanation generation to category inference from justifications, with baseline results using state-of-the-art llms. our experiments highlighted the dataset's value as a challenging, yet important resource for advancing the detection, the explanation, and the mitigation of manipulative textual content in diverse linguistic and cultural contexts. we hope that it will pave the way for future research on narrative analysis by fostering a deeper understanding of how narratives are framed and disseminated, thus contributing to the broader goal of promoting a more informed digital society. in future work, we plan to analyze the narrative elements (the participants, the events, the time expressions, and the relationship between them) in the news articles and in the justifications, with the aim to assess how these structures relate to specific narratives and explanations. additionally, we want to investigate the annotated evidence used to construct these explanations and to conduct extensive analysis and experiments on this data, particularly focusing on generating explanations using these textual segments as inputs.