N arrative M iners at S em E val-2025 Task 10: Combating Manipulative Narratives in Online News (2025)

Abstract: Our team, Narrative Miners, participated in SemEval-2025 Task 10 to tackle the challenge of detecting manipulative narratives in online news, focusing on the Ukraine-Russia war and climate change. We worked on three key subtasks: classifying entity roles, categorizing narratives and subnarratives, and generating concise narrative explanations. Using transformer-based models like BART, BERT, GPT-2, and Flan-T5, we implemented a structured pipeline and applied data augmentation to enhance performance. BART-CNN proved to be our best-performing model, significantly improving classification accuracy and explanation generation. Despite challenges like dataset limitations and class imbalance, our approach demonstrated the effectiveness of hierarchical classification and multilingual analysis in combating online disinformation. We made use of different data augmentation techniques to cover the class imbalances present in the dataset. We had different evaluation metrics set for each subtask, specifically focusing on the need of that particular outcome. With this paper, we hope to play our part in mitigating the impact of harmful disinformation.

## introduction misinformation in online news has become an increasingly urgent concern. manipulative articles can sway public opinion, exacerbate crises, and compromise the reliability of digital content. detecting, classifying, and explaining harmful narratives is therefore a vital step toward combating disinformation. recent advances in machine learning, especially large language models, have made it possible to automate these tasks at scale. our project contributes to the development of these tools to combat the spread of misleading information by providing a deeper understanding of how narratives are constructed and used to shape public discourse. however, the complexity and variety of narratives pose substantial challenges. articles cover-* corresponding author ing major geopolitical events (e.g., the ukraine-russia war) or global issues (e.g., climate change) often embed many subtle manipulative cues within lengthy text. accurately identifying the entity roles, dominant narratives, and subnarratives at play requires a nuanced understanding of context. in this work, we present our approach for semeval-2025 task 10, where we focus on three subtasks: entity framing (subtask 1), narrative classification (subtask 2), and narrative extraction and summarization (subtask 3). our methods leverage recent transformer-based architectures, together with selective data augmentation, to manage real-world complexities such as imbalanced labels and limited high-quality training data. ## dataset we use the official multilingual dataset from semeval-2025 task 10, which comprises approximately 700 news articles in five languages: russian, english, hindi, bulgarian, and portuguese. annotations for this include entity mentions (with roles), narrative labels, and short textual explanations. our primary focus is on the ∼200 english articles, though we also made partial use of the multilingual data for augmentation and validation. ## conclusion the results demonstrate the effectiveness of bartbased models for subtasks 1 and 3, particularly in capturing the framing context of entities in manipulative narratives. further exploration of advanced fine-tuning techniques and hyperparameter optimization is necessary to enhance the performance of transformer models. one major challenge was the limited availability of computational resources, which hindered experimentation with more resource-intensive models like llama. additionally, the small size of the dataset restricted our ability to train models on a fully representative dataset that was large enough to capture all the nuances. because of the serious under representation of some classes we did try training with augmented data where possible, but having more real data would have significantly improved performances.