###### Abstract  
This study aims to address challenges in media monitoring by enhancing closed-set topic classification in multilingual contexts (where both training and testing occur in several languages) and crosslingual contexts (where training is in English and testing spans all languages). To achieve this goal, we utilized a dataset from the European Media Monitoring webpage, which includes approximately 15,000 article titles across 18 topics in 58 different languages spanning a period of nine months from May 2022 to March 2023. Our research conducted comprehensive comparative analyses of nine approaches, encompassing a spectrum of embedding techniques (word, sentence, and contextual representations) and classifiers (trainable/fine-tunable, memory-based, and generative). Our findings reveal that the LaBSE+FFNN approach achieved the best performance, reaching macro-averaged F1-scores of 0.944 \(\pm\) 0.015 and 0.946 \(\pm\) 0.019 in both multilingual and crosslingual scenarios. LaBSE+FFNN's similar performance in multilingual and crosslingual scenarios eliminates the need for machine translation into English. We also tackled the open-set topic classification problem by training a binary classifier capable of distinguishing between known and new topics with the average loss of \(\sim\)0.0017 \(\pm\) 0.0002. Various feature types were investigated, reaffirming the robustness of LaBSE vectorization. The experiments demonstrate that, depending on the topic, new topics can be identified with accuracies above \(\sim\)0.796 and of \(\sim\)0.9 on average. Both closed-set and open-set topic classification modules, along with additional mechanisms for clustering new topics to organize and label them, are integrated into our media monitoring system, which is now used by our real client. closed-set vs. open-set topic classification; multilingual and crosslingual scenarios; well- and lesser-supported languages; media monitoring +
Footnote †: journal: Medical  
The design of our media monitoring system is specifically tailored to meet the unique needs of the client who will deploy it. The system is engineered to handle a wide variety of languages, offering multilingual support for the seamless processing and classification of media content across language barriers. It includes enhanced crosslingual functionality, enabling the topic classification model to (1) adapt to and learn from the available languages and (2) comprehend even those languages not included in the initial training dataset. The system can detect both known and new topics, ensuring their accurate labeling and the effective organization of emerging topics. To achieve the highest possible accuracy, the system's performance undergoes meticulous testing and comparison across various approaches to identify the most effective ones. Additionally, the system operates autonomously to ensure the security of analyzed data and minimize operational costs by reducing reliance on external services. Before starting the research, we identified several potential limitations that must be addressed. Despite the availability of approximately 40 media datasets ([[https://data.world/datasets/media](https://data.world/datasets/media)]([https://data.world/datasets/media](https://data.world/datasets/media)) (accessed on 14 May 2024)) and approximately 60 social media datasets ([[https://data.world/datasets/social-media](https://data.world/datasets/social-media)]([https://data.world/datasets/social-media](https://data.world/datasets/social-media)) (accessed on 14 May 2024)) online, these datasets have some limitations: they are predominantly focused on US content or social issues such as marketing, shopping, and dating. A comprehensive news media dataset covering a broad range of languages and topics is not publicly available. Therefore, we plan to create such a dataset within the framework of this research. We do not intend to train large language models from scratch but will instead leverage available models by fine-tuning them or using them as vectorizers.