bb S tar at S em E val-2025 Task 10: Improving Narrative Classification and Explanation via Fine Tuned Language Models (2025)

Abstract: Understanding covert narratives and implicit messaging is essential for analyzing bias and sentiment. Traditional NLP methods struggle with detecting subtle phrasing and hidden agendas. This study tackles two key challenges: (1) multi-label classification of narratives and sub-narratives in news articles, and (2) generating concise, evidence-based explanations for dominant narratives. We fine-tune a BERT model with a recall-oriented approach for comprehensive narrative detection, refining predictions using a GPT-4o pipeline for consistency. For narrative explanation, we propose a ReACT (Reasoning + Acting) framework with semantic retrieval-based few-shot prompting, ensuring grounded and relevant justifications. To enhance factual accuracy and reduce hallucinations, we incorporate a structured taxonomy table as an auxiliary knowledge base. Our results show that integrating auxiliary knowledge in prompts improves classification accuracy and justification reliability, with applications in media analysis, education, and intelligence gathering.

## introduction the rise of digital media has dramatically reshaped the way information is produced and consumed, enabling direct communication between content creators and audiences. although this has democratized information access, it has also made it easier for manipulative narratives and disinformation to spread, especially during crises and politically sensitive events. news articles often employ implicit messaging, strategic framing, and loaded language to subtly shape public perception @xcite . these covert techniques are not always explicitly deceptive, but instead rely on suggestive phrasing, selective omissions, and emotionally charged language, making them difficult to * these authors contributed equally to this work. ## related work the task of extracting dominant and sub-narratives from text and also generating free-text explanations that justify a dominant narrative within a text article falls under the broader domain of computational narrative extraction and discourse analysis. these are fundamental tasks in nlp and we have explored research on multilabel text classification, explainable ai (xai) for text generation, retrieval-augmented generation (rag), and re-act prompting techniques. ## task description understanding implicit narratives in news articles is essential for detecting bias, framing, and potential manipulation. this task focuses on two key challenges: multi-label classification of narratives and sub-narratives (subtask 2) and generating concise, evidence-based explanations for dominant narratives (subtask 3). in subtask 2 (narrative classification), given a news article and a predefined two-level taxonomy of narratives and sub-narratives, the goal is to accurately assign all relevant sub-narrative labels to the article. this is a multi-class, multi-label classification problem where both the primary narrative and its sub-narratives must be correctly identified. in subtask 3 (narrative extraction), given a news article and a dominant narrative, the goal is to generate a brief, text-based explanation (maximum 80 words) supporting the dominant narrative. the generated justification must be grounded in the article by referencing textual evidence that aligns with the claims of the dominant narrative. both subtasks are crucial for enhancing media analysis, fact-checking, and disinformation detection by providing structured narrative classification and transparent, text-grounded justifications. ## methodology this section describes our approach to solving the two subtasks: (1) narrative classification, where we assigned narratives and sub-narratives to news articles in a multi-label classification setup, and ## results we evaluated our two tasks-multilabel classification and the generation of evidence-based explanations for narratives-using f1 scores. the approach with the highest f1 score was chosen as the objective, as we aimed to balance precision and for the text generation task, we utilized bertscore @xcite to compare results, as it measures semantic similarity between strings using contextual embeddings. unlike ngram-based metrics (e.g., bleu, rouge) @xcite , which struggle with paraphrased or implicit reasoning, bertscore effectively captures meaning equivalence by leveraging deep contextual representations. ## conclusion this study advances nlp-driven narrative analysis by introducing a framework for classifying and justifying implicit narratives in news articles. our multilabel classification approach, fine-tuning bert-base-uncased with a prompt-engineered llm, effectively identified dominant and subnarratives. the framework maintained strong performance even with gpt-4o-mini, demonstrating the scalability and adaptability of the system without significant performance compromises. this lightweight configuration reduces computational overhead and enables deployment in resourceconstrained environments, making the framework practical for real-world, large-scale applications. future implementations can further optimize resource usage by incorporating retrieval caching mechanisms and distributed modular processing across subtasks. furthermore, our narrative justification approach, which combines semantic similarity search with a react-based reasoning structure and auxiliary knowledge retrieval, significantly improved text generation quality across multiple languages. the model consistently outperformed the baseline methods in bert f1, underscoring the effectiveness of integrating contextual retrieval mechanisms with generative reasoning to generate coherent and factually aligned justifications. these findings improve media analysis, automated content understanding, and intelligence gathering by improving the detection of implicit ideological framing. as nlp advances, our approach lays the groundwork for more transparent, explainable ai-driven media analysis, supporting efforts to combat misinformation and strengthen media literacy across diverse linguistic and cultural contexts. looking ahead, future work will investigate the use of dynamic crowd-sourced knowledge bases and adversarial testing to identify and minimize potential biases introduced via semantic retrieval or taxonomy-driven prompting. this will further ensure the fairness, robustness and generalizability of the system across sociopolitical domains and multilingual settings.