K ostas T hesis2025 at S em E val-2025 Task 10 Subtask 2: A Continual Learning Approach to Propaganda Analysis in Online News (2025)

Abstract: In response to the growing challenge of propagandistic presence through online media inonline news, the increasing need for automated systems that are able to identify and classify narrative structures in multiple languages is evident. We present our approach to the SemEval-2025 Task 10 Subtask 2, focusing on the challenge of hierarchical multi-label, multi-class classification in multilingual news articles. We present methods to handle long articles with respect to how they are naturally structured in the dataset, propose a hierarchical classification neural network model with respect to the taxonomy, and a continual learning training approach that leverages cross-lingual knowledge transfer.

## introduction from early days, propaganda has been a tool in shaping people's beliefs, actions, and behaviours. the most effective propaganda techniques often go undetected, influencing readers without even their knowledge @xcite . with the rapid growth of the internet and the web revolutionizing the way people share and access information, it has also opened doors to propagandistic techniques being disseminated more effectively, reaching vast audiences worldwide @xcite . at research level, most work on propaganda detection has focused on high-resource languages like english, and little effort has been made for low-resource languages. similarly, previous work examined content at the document level @xcite , where they work focused on analyzing entire articles to differentiate between propaganda, trusted news, and satire rather than analysing specific narrative structures. semeval-2020 task 11, which focused on propaganda and news analysis, was introduced to address this @xcite featuring the classification of portions of documents across 44 propagandistic techniques. semeval-2025 task 10 foot_0 @xcite was introduced as a significant advancement that focuses on the automatic identification of specific narrative structures, their classification, and the roles of entities involved in online articles in a multilingual setting. this study focuses on the narrative classification subtask of semeval-2025 task 10. unlike previous tasks, it centers around the identification of both the broader narratives of articles and their specific subnarratives. in this paper, we explore how hierarchical mlps can model this nested taxonomy structure, investigate methods for handling long article inputs, and examine how different language orders can affect model performance in a continual learning training strategy. researchers have studied whether language order affects catastrophic forgetting in continual learning, but optimal order could vary across tasks and language sets. our research builds on their findings, attempting to address the following research question: "is there an optimal language order in language-specific continual learning for narrative classification? if so, which is the best and which is the worst?" during our participation in the challenge, our primary approach, consisting of an ensembled version of a continual learning training strategy was evaluated in five languages achieving top-five rankings in across all languages 2 . our analysis revealed that the order in which our model is trained matters significantly in model performance, with certain language orders outperforming others. ## experiments and results below we present the comparison results across model variants, embedding models, and aggregation strategies. we report both coarse-f1 (for narratives) and fine-f1 (for subnarratives), along with their standard deviations. however, the primary focus of the task is on the fine-f1 score. all comparisons are performed specifically for the english validation dataset, as it demonstrated the most balanced distribution of narratives in the dataset across the two domains and is widely recognized as the most prominent language in nlp research. each model was run five times, and the results were aggregated to ensure a fair comparison. we evaluated our experiments with two embedding models: kalm foot_2 and stella foot_3 . we specifically chose these embedding models because they are both multilingual, instruction-based that achieved high performance on the mteb (massive text embedding benchmark) leaderboard foot_4 . during the stage of transforming our article sections into meaningful numbers that our classification models can understand, we instructed the embedding models to: