News media are a powerful source of information shaping perceptions and behaviors of society. The amount of news content is increasing daily, from traditional high-quality news to less-reliable social media content. Media monitoring and analyses need to be performed in real-time: grouping articles by their content, adding several categories of meta-information, summarizing several news sources, performing analyses, and reporting. Recent advances in the field of natural language processing, in particular the development of large pretrained language models, allow for the development of automated tools that can accurately process and categorize text in a range of ways (e.g., in terms of topics and sentiment), and generate summaries from multiple sources. However, even the best of these tools need to be improved in their ability to cope with the complexity of the news category hierarchies, metadata structures used in the news industry, adaptation to specific user needs, and coverage of multiple languages. In this applicative project with Kliping, d.o.o., we develop advanced multilingual news and social media content analysis tools to help automate these processes while increasing societys ability to understand the rapid flow of information surrounding us.